Inter-American Development BankBanco Interamericano de Desarrollo

Latin American Research NetworkRed de Centros de Investigación

Research Network Working paper #R-443

Rentier States and GeographyRentier States and GeographyRentier States and GeographyRentier States and Geographyin Mexico’s Developmentin Mexico’s Developmentin Mexico’s Developmentin Mexico’s Development

by

Roberto BlumAlberto Díaz Cayeros

Centro de Investigación para el Desarrollo, A.C. (CIDAC)Centro de Investigación para el Desarrollo, A.C. (CIDAC)Centro de Investigación para el Desarrollo, A.C. (CIDAC)Centro de Investigación para el Desarrollo, A.C. (CIDAC)

March 2002

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Cataloging-in-Publication data provided by theInter-American Development BankFelipe Herrera Library

Blum, Roberto.Rentier states and geography in Mexico’s development / by Roberto Blum, Alberto

Díaz Cayeros.

p. cm. (Research Network Working papers ; R-443)Includes bibliographical references.

1. Mexico--Economic conditions--Effect of Administrative and political divisions on.2. Mexico--Economic conditions--Econometric models. 3. Economic geography--Econometric models. I. Díaz Cayeros, Alberto. II. Inter-American Development Bank.Research Dept. III. Title. IV. Series.

330.9 B385--dc21

�2002Inter-American Development Bank1300 New York Avenue, N.W.Washington, D.C. 20577

The views and interpretations in this document are those of the authors and should not beattributed to the Inter-American Development Bank, or to any individual acting on itsbehalf.

The Research Department (RES) produces the Latin American Economic PoliciesNewsletter, as well as working papers and books, on diverse economic issues. To obtain acomplete list of RES publications, and read or download them please visit our web site at:http://www.iadb.org/res

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Table of ContentsIntroduction

1. Theoretical Linkages between Geography and Development

2. A Conceptual Model: Natual Ecology and Institutional Ecology as Interfaces between

Geography and Economic Development

3. The Mexican Case: A Historical Account

4. The Mexican Case: A Statistical and Geographic Profile

5. Regression Analysis of the Effects of Geography on Income, Growth and Poverty

a) State GDP Levels in 1993

b) Poverty Levels in 1990

c) Growth of GDP Regressions from 1950 to 1993

d) GDP Levels in 1980

e) Regional Inequality Decomposed

6. The Meaning of Municipalities

7. Policy Implications and Research Agenda

8. References

Appendix. Descriptive Profile of GDP and Geography in Mexico, and Full Set of Results

for Section 5

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List of Tables and Graphs

Figure 1. Natural and Institutional Ecology

Figure 2. Average Rainfall

Figure 3. Climates

Figure 4. Average Temperature

Figure 5. Effect of Average Rainfall on Per Capita GDP

Figure 6. Resilient Political Jurisdictions

Table 1. Summary Statistics of Mexican state GDP by Various Geographic Factors

Table 2. Physical Natural Resource Balances

Table 3. Net Domestic Environmental Product by Sector 1995

Table 4. Geographic Determinants of State GDP Levels 1993

Table 5. Correlation of Infrastructure and Human Capital Variables

Table 6. Land Types and State GDP Levels in 1997

Table 7. Fractionalization of Land Types and Ejido Organizations for Agricultural

Production as Determinants of 1993 GDP

Table 8. Geographic Determinants of State Poverty Levels 1990

Table 9. Geographic Determinants of State Growth 1950-1993

Table 10. Geographic Determinants of State GDP Levels 1980

Table 11. GINI Coefficients Isolating Effects of Explanatory Variables

Table 12. Relative Contribution to Inequality Measured by GINI Index

Table 13. Number of Municipalities by State

Table 14. Evolution of Territorial Division from the Late Nineteenth Century to the

Twentieth Century.

Table 15. Historical Origin of Number of Municipalities

Table 16. Correlation Matrix of the Number of Municipalities and Other Institutional

Density Variables

Table 17. Municipalities as an Explanation for Public Good Provision

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Abstract*

This paper provides a long-term historical and econometric account of theway in which geography has shaped development in the Mexican states. Theemphasis is placed on the way in which the natural geography is reinforcedby political decisions, which configure the human geography of populationdensity, urbanization and public good provision, which in turn determineincome, growth and poverty. The paper presents brief historical instances ofhow geography has determined prospects for development at differentmoments in Mexican history. This anecdotal discussion seeks to highlightthe intrinsic link of geography with political institutions, which is central tounderstanding the economic effects of geography. The paper then presents adescriptive statistical and geographical profile of the relationship betweengeography and development in Mexico. Econometric estimates of the effectsof geography on income, growth and poverty are used to decomposeregional inequality, separating the specific contribution of naturalgeographic factors (climate, location and population density) as compared topublic good provision, in the form of urbanization and literacy, and politicalarrangements, as reflected in the fragmentation of political jurisdictions. Thepaper argues that the main channel through which geography affectsdevelopment is political. The fragmentation of political jurisdictions in theform of municipal governments constitutes a proxy for man-made barriers togeographic mobility, which explain the interaction between geography,politics and development.

* We acknowledge research assistance of Diego Steinhendler in data processing and preparation of tables.Comments from John Lake Gallup, Alejandro Gaviria, Edna Jaime, Claudio Jones, workshop participants inthe Geography and Development seminar held in Cuernavaca, Mexico, May 27-28, 1999 and otherresearchers at CIDAC are greatly appreciated. Eduardo Lora deserves special thanks for reading and rereadingprevious versions of the paper, offering always insightful comments. Of course all errors remain our soleresponsability.

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Introduction

When Hernán Cortez was asked by Charles I of Spain to describe the new land he had

conquered, he is said to have taken a piece of parchment and crumpled it to illustrate the

rugged aspect of the landscape. Microhistorian Luis Gonzalez has argued that Mexican

history can only be understood as the stories of three hundred valleys that make up the

matrias, or motherlands, to which Mexicans really pay allegiance. At the beginning of the

twentieth century, dictator Porfirio Díaz was well aware of the power of geographic destiny

when he lamented, “poor Mexico, so far from God and so close to the US.” Geography has

undoubtedly played a key role in the long-term economic development of Mexico, while

the influence of geography continues to be felt in contemporary patterns of development

such as the location of new industry in Northern mid-size cities, increasing migration flows

from Southern Mexico and the central high-plateau to the Northern border region and the

coastal areas, environmental degradation in some of the poorest regions in the South, or the

intricate flows of temporary migration to specific cities in the US from particular regions of

the country.

Despite its importance, the impact of geography on development has been little

discussed in Latin America or Mexico. This paper aims to provide some insight into the

relative importance of geographical conditions for Mexico’s long-term economic and

political performance. Political performance is central to the study because we believe

geographic conditions determine some of the configurations of political forces and

institutional arrangements that have led to particular economic policies, especially in the

realm of taxation and the fragmentation of political jurisdictions. Those policies, in turn,

hinder or promote economic development. We call those arrangements a country’s

“institutional ecology,” a system of interrelated institutions built over time to achieve

certain socially desirable purposes.

We are not, however, geographic determinists. Long-term processes of economic

development are not the product of one causal factor, but instead result from

multidimensional social, political and economic forces. Grasping those processes requires a

multidisciplinary approach, combining various theoretical approaches, and the interaction

of historical, social, political and economic evidence. We have chosen to put geographic

factors in a prominent position, however, in order to reassess their importance.

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Using data from Mexican states and municipalities, the paper tests some of the

hypotheses of Gallup and Sachs (1999), Bloom and Sachs (1998) and Gallup (1998)

concerning the role of geography in development. We go beyond econometric estimates,

though, providing a long-term historical account of the process whereby geography shaped

political arrangements, which produced specific policies with clear developmental impacts

on the Mexican states. In particular, we believe that the fragmentation of political

jurisdictions in the form of municipal governments constitutes a good proxy that explains

the interaction between geography, politics and development.

The paper is organized as follows. The next section provides a brief overview of

several recent contributions to the theoretical literature on linkages between geography and

development. The section additionally explores the implications of the literature reviewed

for the Mexican case. Section 2 is also theoretical in nature, briefly sketching a General

Systems Theory model (drawn from Blum, 1999) to study the effects of geography on

development. The systems model suggests that the interface between geography and

development is to be found in the natural and “institutional” ecologies of society. Both

theoretical discussions seek to provoke thoughts on how to approach geography and

development, rather than providing a definite explanation of the causal linkages between

geography, political institutions and economic performance.

Section 3 presents brief historical instances of how geography has determined

developmental prospects at different moments in Mexican history. This discussion seeks to

provide evidence of the intrinsic link of geography with political institutions, which is

central to understanding the economic effects of geography. Section 4 presents descriptive

statistical and geographical evidence on the relationship between geography and

development in Mexico. Section 5 provides econometric estimates of the effects of

geography on income, growth and poverty. This section decomposes regional inequality in

Mexico, separating the specific contribution of geographic factors (climate, location and

population density). Section 6 explores the relationship between the fragmentation of

political jurisdictions in the form of municipalities, geography and development. That

section assesses the political channel through which geography affects development.

Section 7 provides policy implications and an agenda for future research.

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1. Theoretical Linkages between Geography and Development

Geographic conditions have come to play a new theoretical role in explaining long-term

economic development and growth. A seminal contribution, still widely cited by economic

geographers, is Douglass North’s (1955) explanation of the way in which the resource base

determines possibilities for specialization in world trade. North’s initial insights were

subsequently developed mostly in an institutional direction, explaining long-term economic

performance as a consequence of political institutions protecting property rights and

generating competitive markets (North and Thomas, 1973; North, 1981; Landes, 1988).

However, North’s account of growth in the United States (1966) as a virtuous institutional

arrangement, whereby three regionally specialized economies were able to profit from trade

within a federal arrangement, gives a greater explanatory role to the resource base than his

later works. In fact, one of the main questions North (1990) poses in his Institutions,

Institutional Change and Economic Performance, regarding why the economic

performance of two seemingly similar countries at the beginning of the nineteenth century,

the U.S. and Mexico, diverged in such a striking manner, can probably be best answered if

one combines institutional and geographical factors.

A variety of recent works by economists have explored the role of geography.

These include the influential papers by Paul Krugman (1991) that revived interest in the

role of geographic location in trade theory. In addition, several papers coauthored by

Alessandra Casella (Casella and Frey, 1992; Casella and Feinstein, 1991) have studied the

effect of political jurisdictions on the territorial distribution of economic activity; and, of

course, the booming literature on regional convergence has been motivated by Roberto

Barro and Xavier Sala-i-Martin’s (1994) empirical work on economic growth.

Environmental and weather conditions have additionally been highlighted in the recent

literature. Sachs and Warner (1995), for example, have called attention to the role of

droughts as a purely natural shock with momentous economic effects, particularly evident

in sub-Saharan Africa. Also in Africa, growth regressions have increasingly incorporated

aspects such as wars, economic and political conditions of neighboring countries,

ethnolinguistic fractionalization and characteristics of physical infrastructure, which are

geographically given (for a review, see Collier and Gunning, 1998). The recent papers in

the project on geography and economic development at the Harvard Institute of

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International Development (Gallup, Sachs and Mellinger, 1999; Sachs, 1997; Radelet and

Sachs, 1998; Bloom and Sachs, 1998; and Gallup, 1998) have provided a more

comprehensive research agenda in these areas, and the recent IADB (1998) report on

inequality provides evidence of the importance of geography in the development of Latin

America.

In a more provocative approach, evolutionary biologist Jared Diamond (1997)

provides an explanation of how environmental geography has influenced societal

development throughout history. Diamond’s view is somewhat extreme when applied to

historical and social processes, but his main insight on taking geography as a serious barrier

or facilitating condition for human development is powerful, as his work convincingly

shows. On the environmental front, the Brundtland report some years ago was highly

influential in focusing questions of poverty and destitution as direct consequences of a

vicious circle in which destitution produces degradation of the environment and

environmental damage leads to further impoverishment of the already poor. This has made

scholars interested in poverty and destitution more aware of the conditions of the

environmental resource base (Dasgupta and Maler, 1995).

Geographical conditions do not change quickly, even though human transformation

of the environment can open up vast expanses of land to agriculture or cattle raising;

construction of roads and bridges might provide for the mobility of goods and services;

swamps are drained, rivers are dammed, forests are burned or otherwise cleared. In general,

however, geography remains very much the same in the short term, or at least in periods for

which reliable regional statistical information might be available. Some resources, like

minerals, are only exploited once a technology is found to process them. Some regions

might be physically isolated only because a road or railroad has not been built. In this

context, long-term performance must be measured not just in decades, but probably

centuries. Hence the Mexican case is discussed taking into account a long historical period

beyond that for which regional statistical information is available.

Gallup, Sachs and Mellinger (1999) consider four areas in which geography might

play a direct role in economic productivity: transportation costs, human health, agricultural

productivity, and proximity and ownership of natural resources. In Mexico, the

performance of regional economies has been directly affected by analogous factors. Water

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and soils have determined the character of agriculture. Given the rugged topography and

the absence of internal waterways, communication across regions has depended on

transportation costs. In the colonial period, the location of mineral deposits determined the

most important axis of internal trade flows. Energy resources conditioned the location of

the first efforts at industrialization at the turn of the last century in the city of Monterrey,

where nearby coal was available, and oil booms have had definite regional impacts on the

industrialization of Veracruz during the 1910s and 1920s or in Tabasco during the last

decade. Gallup, Sachs and Mellinger report not finding a direct effect of geography and

health variables that would explain the shortfall in Latin America’s economic growth

relative to Asia (1999, p. 27). However, it is likely that the level of aggregation of the

heterogeneous regions making up the large Latin American countries creates a fallacy of

composition that hides geographic effects that could be studied at the regional level.

Regarding indirect effects of geography on economic development, Gallup and

Sachs suggest that some economic policies, particularly tax policy, might be endogenous to

geography. In particular, they provide a model in which the “optimal tax is an increasing

function of transport costs, discount rate, and the probability of losing office; and a

decreasing function of total factor productivity and the responsiveness of growth to the tax

rate.” This means that predatory elites, which in Olson’s terms (1993) behave as roving

bandits, are more likely to be present when regions are isolated and when they face many

challengers. This is the pattern observed during the nineteenth century in Mexico.

Cacicazgos (domains of local bosses) emerged probably as a second best solution, through

which tax rates could be decreased because time horizons were long and the hold on office

was secured, but the arrangement depended on making a region relatively impermeable and

isolated. Internal tariffs (the long-lasting alcabalas in Mexico) might have been imposed

not as a source of revenue, but as a way to keep transport costs high and maintain the

caciques’ hold on power. The seemingly irrational economic policies carried out in Mexico

during much of the nineteenth century and the first half of the twentieth century regarding

internal barriers to trade might thus be endogenized as part of the natural configuration of

resources. In fact, the fragmentation of political jurisdictions in the form of municipalities

emerging in the early nineteenth century might be explained to some extent by geographic

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conditions, coupled with the ruler’s desire to establish internal barriers to the movement of

goods and services.

This leads to a major area of study related to trade liberalization. One of the central

hypotheses of recent studies is that a coastline allows for greater contact with other

countries, hence regions near the coast will tend to promote policies of free trade. In the

Mexican case, however, the coastline seems to have been of little consequence for trade

flows. This is explained first by Mexico’s tropical location, which until very recently made

most of its coastline highly unhealthy. Tropical illnesses regularly decimated coastal

settlements, and as a result few towns were established in the lowlands. Colonial Mexican

ports were few in number and primarily served the shrinking international trade between

the Spanish colonies in the Pacific area and the metropolis. Second, trade within the

country did not proceed through the coastal waterways because its sparsely populated

coastlines and formidable mountain barriers blocked communication to the more populated

central high-plateau settlements. However, a very different coastline has emerged in the

recent past, that of the border with the U.S. Although the Rio Bravo (known in the United

States as the Rio Grande) is not navigable, the quality of infrastructure in the US has

effectively transformed border cities into ports open to the global economy. The possibility

of trade in international markets constitutes one of the most important escapes from adverse

natural geographical conditions.

2. A Conceptual Model: Natural Ecology and Institutional Ecology asInterfaces between Geography and Economic Development

The need to trace and explain the multiple pathways whereby geography influences

economic development requires the construction of a whole set of conceptual models.

Providing all the interlinked models highlighting different aspects of these influences is

clearly impossible. General Systems Theory (GST) allows, however, for this kind of

complex and interdisciplinary modeling endeavor. From the simplest possible models in

GST—integrating various “inputs” of energy, materials and information, their processing

inside “black boxes” to produce the observable results, “outputs” consisting of the same

three kinds of elements—to the very detailed and sophisticated “flow models” necessary to

produce computer simulations, GST is a very useful instrument for understanding complex

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phenomena. We thus draw from a general but simple model, provided by Blum (1999),

emphasizing “natural ecology” and “institutional ecology” as the interfaces between

geography and economic development.

Figure 1. Natural and Institutional Ecology

nat ural ecology

Terr it ory

Populat ion

Inst it ut ions Set of Purposes

cont rol governance

:

inst it ut ional ecology

growt h legit imacy

Figure 1 depicts both “natural” and “institutional” ecologies as the interfaces

between the territory (geography) and population (social interactions), which together

produce the phenomena we call growth and legitimacy. These social products cannot be

well understood outside the innumerable and complex interactions occurring among the

different elements of the system. Such interactions include, among many others, the way in

which political processes are structured and boundaries between jurisdictions are created

and enforced, often following “natural” barriers; the way in which production is organized,

including the establishment of property rights over natural resources and raw inputs used in

the production process; and the way in which tax resources are extracted, usually by the

force of the state.

It is clear that in the long run humans are able to radically transform the natural

environment. Not only are dwellings and roads built, rivers dammed, forests felled and

mineral resources extracted, but humankind has established various symbiotic relations

with other living species and thus multiplied its own effects on geographical features.

Intensive mining processes and extensive cattle raising during the 300-year colonial period

(1521-1821), for example, destroyed a large part of the original temperate forests existing

in Mexico. Mezquite and low brush vegetation colonized large parts of the Northern

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region. Land erosion proceeded at a fast pace around mining towns, and local rainfall

patterns changed during this period. An even more dramatic transformation of geography

is observed in the central valley of Mexico. Its originally numerous lakes were deliberately

drained to build Mexico City. Even now, people are still alive who remember a time in

which rivers and creeks criss-crossed the Mexican capital and clear lakes surrounded the

growing metropolis, now a veritable ecological catastrophe in the making. The “natural

ecology” or environmental interface is the conceptual surface at which geography and man

interact. Though geography is usually the passive extreme of this relationship, natural

ecology is always interactive.

The other conceptual interface we need to consider is “institutional ecology.” This

is the interrelated system of institutions built over time to achieve certain socially desired

purposes. The institutional ecology comprises a large diversity of: a) institutions, formal

and informal, b) rules and meta rules, c) allowable group strategies and tactics, legal or

illegal, and d) social and individual interests that continuously interact, creating some kind

of stable equilibrium. Institutional ecologies can be more or less dense, according to the

sheer weight of their different parts. A denser ecology is that which has built over time

more institutions in a certain conceptual or real geographic space. A higher institutional

density correspondingly implies greater costs of constsruciton, maintenance and

transformation.

Institutional density implies “institutional inertia,” which explains the difficulty of

changing the setting as fast as conditions might require. A “lock-in” phenomenon occurs

that might explain the different paths historical societies have taken. An institutional

ecology can also be considered more or less robust, which implies the system’s ability to

keep its coherence over time, and robustness appears to be a function of institutional

diversity. The number of municipalities emerging in the different regions in Mexico is thus

a proxy measure to assess institutional density and the diversity of institutional

arrangements emerging over time. In regions of recent settlement, where individual private

property rights over land were established and towns were sparse, few municipalities were

created. Where indigenous populations predated the institutional framework of the national

state, communal property rights existed and population density was high, large numbers of

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municipalities were the rule. The difference in municipal configurations in Mexico

reflected geographic, ethnic and political realities.

The interfaces of natural ecology and institutional ecology imply hidden and overt

costs for society. Until recently, ecology was not even considered when national or

regional economic accounts were produced. Environmental advocates have been able to

bring to the forefront the need to integrate into the process of economic accounting the

hidden—but not insubstantial—environmental costs. When these are considered, a more

realistic evaluation of economic performance can be achieved. On the other hand, the

institutional ecology costs have always been considered, though not formally, by decision-

makers.

For example, public good provision in Mexico has been hindered by the

fragmentation and dispersion of population, as reflected by municipal organization. It has

been obvious that institution building and its transformation imply real costs to society.

Though there are still no explicit and generally accepted methods to cost institutions,

neither for their building or maintenance nor for the economic growth or stagnation they

produce, the inclusion of both the ecology and institutional ecology costs is conceptually

necessary to provide a better context for the analysis of economic development.

Tax systems and tax structures provide one area where including both natural and

institutional ecology in the understanding of the development process might prove

particularly fruitful. Revenue extraction is one of the oldest problems rulers face, and the

natural and institutional characteristics of a given society might determine how this is done,

and more importantly, what consequences a fiscal structure has on economic performance

over time.

3. The Mexican Case: A Historical Account

A few historical instances for which good research and evidence exist illustrate the

interaction of geography and social activity, which produce ecological transformations and

facilitate policy decisions and institution-building. Over time, though, these processes

produce “lock-in” phenomena that persist for long periods even though conditions have

drastically changed.

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A first example comes from the extensive research carried out mostly by social

anthropologists (Palerm, 1952, Wittfogel, 1981, and Harris, 1985, among others) on pre-

Hispanic societies in the central region of Mexico. Though their methodologies differ, they

have seriously considered the “Marxian” concept of the “hydraulic societies” as a

beginning point for their social analysis. The existence of numerous lakes in the Central

valleys of the Mexican high plateau and the urgent need to control the regular but

devastating floods that occurred, as well as use those same waters for irrigation, initiated a

process of institution-building that produced the societies the Spaniards found when they

reached Mexico in the sixteenth century.

Though these societies—the Aztecs, Mayans, Mixtecs, Zapotecs and Tarascans—

greatly differed among themselves, they all shared some social organizational

characteristics that were analogous to the classical hydraulic societies already being studied

in Asia. All of these were “despotic” societies with large bureaucratic structures,

supported by a large mass of landless peasants working small, communally held plots.

These societies developed into centralized pre-states that depended on the tribute of many

conquered peoples. Though these cultures’ technology was primitive (e.g., metals were

unknown), their social institutions were not. Great cities and large populations attest to the

efficacy of these institutional complexes. As a result, the Spanish conquistadores were able

to adapt many of the indigenous institutions they found to the Crown’s and their own

purposes. Thus, present-day Mexico cannot be well understood without considering the

institutional “lock-in” phenomenon.

The regions where these “semi-hydraulic” pre-Hispanic civilizations were

established, the Yucatan peninsula and the central valleys of Mexico, Michoacan, and

Oaxaca, remain to this day areas where “institutional density,” as measured by the number

of municipalities, is above the national average. In these regions it is still possible to

observe some of the archaic institutions, adapted or not, to the present conditions of modern

Mexico. For example, the ejido, the most common form of land tenure in modern Mexico

and a product of the 1910 Mexican revolution, is the direct descendant of the marriage of

the Spanish medieval ejido, common land assigned to the townships, and the pre-Hispanic

calpulli, state-held property worked by individual families.

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This communal form of land tenure has maintained its central purpose for over 700

years. Though food production has been an important byproduct of this institution, its

central purpose has been to maintain control over the tens of thousands of peasant

communities that exist in Mexico. Control of these populations originally was needed to

build and maintain the hydraulic infrastructure that pre-Hispanic societies and Colonial

Mexico needed for their survival. When the natural ecology changed, the lakes were

drained and the chinampas (floating gardens) became just a tourist attraction, however, the

institutions created a long time before remained and their original purpose was redirected to

serve new needs.

The PRI, the party established by the “revolutionary family” in the late 1920s,

successfully used the ejido to keep political control of the country for over 70 years.

Discretionary land distribution among poor peasants and the subsequent creation of new

ejidos has rooted the growing Mexican rural population to specific regions and transformed

them into clients, first of the local political “bosses” and then of the agrarian federal

bureaucracies developed to control and extract rents from them.

A second example of geography and development producing important institutional

changes can be found in the Bajio region of north-central Mexico during the early years of

the nineteenth century. The region is a fertile valley traversed by the Lerma river. The

land is mostly flat, and the region is relatively close to the mining towns of Guanajuato,

Queretaro, San Luis Potosi, Zacatecas and Pachuca. Its population grew rapidly and

developed modern agriculture to feed the surrounding booming silver mining towns. By

the end of the eighteenth century, the Bajio was the breadbasket of Mexico. Thus we can

observe two very different economies symbiotically united in that relatively small area. On

one hand there were the mining towns’ economies, sustained by the exploitation of silver

and subject to the rentier state. On the other hand was a more modern agricultural economy

made up of large private haciendas and small independent ranchers that were producing

agricultural value through their hard work and improved technologies. These rancheros

were subject to a more limited and modern institutional ecology.

The Bajio’s independent agricultural producers depended on the continuous flow of

work capital provided by the Catholic Church, a large rentier with excess liquidity that

provided many financial services to society at a moderate rate. The Church as a financial

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institution had a long term horizon, and its credits to the Bajio farmers were renewed

routinely. However, in the early 1800s the King of Spain ordered the Mexican Church to

provide him with a “forced” loan to pay for the European wars in which he was engaged.

When the Church began to call in its loans, the Bajio was suddenly plunged into a liquidity

crisis, and tens of thousands of modern agricultural producers were left financially exposed,

with resulting discontent throughout the region. Many Church leaders sympathized with

them, and a potent revolutionary brew began to boil. Thus the war of independence had

found a fertile ground in the Bajío, the crossroads of two different economies, the rentier

and the modern limited. Those individuals used to working under a modern limited

institutional ecology would not readily accept the heavy-handed approach of the rentier

state structure. Significantly their battle cry was “Long live the King, down with bad

government.”

This conflict, between a heavy-handed rentier state and social and economic agents

developing in a more modern limited institutional ecology, has been a constant in Mexico’s

modern history. Though in many aspects different, the 1910-20 Mexican revolution

showed an eerie similarity to the underlying causes of previous social conflicts. In this

second “revolution,” modernizers (Monterrey’s new industrialists, Sonoran export-oriented

ranchers, discontented Northern politicians, labor unionists and the incipient urban middle

class) teamed with “archaists” in Veracruz, Morelos and Oaxaca, who longed for a more

simple and community-oriented society, to disrupt an institutional setting that either was

not modern enough or too modern for the vastly different communities and populations

comprising the Mexican nation.

As early as 1915, still in the midst of the armed phase of the “revolution,” land

reform laws began to be enacted in northern Mexico, especially the Gulf and Pacific coast

areas, where the land’s natural conditions allowed for modern export-oriented agriculture.

The overt purpose of these early statutes was to promote the modernization of agricultural

processes and increase their general productivity by dividing the mostly underdeveloped

latifundia and distributing unproductive surplus lands among the peasants mobilized by the

revolutionary armies, thus promoting the colonization of the barely populated North. This

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first phase of the agrarian reform1 was led by a heterogenous group of revolutionary leaders

who originated mostly in Mexico’s northern region. These northerners took control of

Mexico until 1934, when Lazaro Cárdenas assumed the presidency. The second phase of

the agrarian reform began during the 1930s. Different purposes were behind the enactment

of the later agrarian reform laws, which were designed mainly for Mexico’s relatively more

populated central area. In this phase, the federal government wanted to achieve two main

purposes: 1) to mobilize rural resources, capital, entrepreneurship and labor to the urban

markets to boost the industrialization program being developed, and 2) to establish an

efficient corporatist structure of political control over the large peasant population.

Thus, the Mexican agrarian reform really comprises two different sets of

phenomena and policies, originally designed and implemented by two different political

groups and attuned to two different natural and institutional ecologies. It served different

purposes and affected Mexico’s development in different ways. While a strong case can

probably be made regarding the impact of specific geographical conditions on the agrarian

policies implemented in Mexico throughout the past four centuries, this impact seems

especially clear in the two post-revolutionary phases of land reform, whether in terms of

tenure or distribution.

Mexico is still torn by the conundrum of its enormous geographical differences and

the “locked-in” rentier institutional ecology that has developed over the centuries. The

costs of transforming this institutional setting are enormous, although not transforming

them can be even more costly in terms of the “natural ecology” disaster that is emerging

and the opportunity cost of development not achieved because of the many barriers and

obstacles generated by existing institutions.

1 Although a very early program of land distribution was enacted in the central states of Morelos, Mexico andTlaxcala to provide land to the peasant rebels that formed the bulk of Emiliano Zapata’s army, land reform inthe early stages was attuned to the needs of development of Northern Mexico.

20

4. The Mexican Case: A Statistical and Geographic Profile

Most of Mexico is a tropical country characterized by great regional diversity, a landscape

of natural barriers between regions, and a very dense institutional ecology. The 3,000 km

border with the US makes the country unique in that flows of trade, migration and goods

and services in general are largely determined by the rich neighbor to the North. The stable

political arrangements that have characterized Mexico over much of the last 500 years have

been historically punctuated by periods of upheaval. Notwithstanding wars of

independence, reformation, the revolution, and the Cristero movement, political institutions

have been extremely resilient to change, particularly in terms of informal practices that are

still observed, mostly in some of the less developed regions. This is the “institutional

ecology” in the country, which was briefly discussed above.

In relation to weather, the most prominent feature of Mexico’s geography is the

wide variation in rainfall, temperature and climates characterizing the country. The maps in

Figures 2 to 4 depict the geography of average temperatures, average rainfall and climates

in Mexico. These climatic profiles are also related to the nature of the terrain, with

mountain ranges running North to South on both the East and the West, creating a

temperate central plateau, which can be clearly distinguished from the tropical and semi-

tropical coast and the desertic North. We can thus think about the country as divided into

three major geographic areas: tropical coastal regions, the dry and warm North, and the

relatively temperate central highlands.

21

Figure 2. Average Rainfall (in mm)

22

Figure 3. Climates (Hot, Dry and Temperate)

23

Figure 4. Average Temperatures (Centigrade Degrees)

24

This variation of climates might be related to GDP by state. Tables 1-5 provide

calculations of the 1997 per capita GDP (in 1996 pesos) for the Mexican states according to

latitude bands, temperature, range of average rainfall, GDP per square kilometer and an index of

trade mobility.2 Latitude bands, temperature, average rainfall and surface data come from INEGI,

while the index of trade mobility is calculated by Díaz Cayeros (1995). Since there is no single

statistic that best summarizes geographic variables (the mean, median or standard deviation do

not fully capture the distribution underlying those natural features) we present ranges of those

variables. Hence Table 1 provides a summary of how Mexican GDP is distributed according to

various criteria of ranges of latitude bands, monthly variability of temperatures and rainfalls in

the capital city of each state and a trade mobility index calculated as the combination of paved

roads, railroad tracks, and number of cars circulating (aforo vehicular) per square kilometer.

Table 1. Summary Statistics of Mexican State GDP by Various Geographic Factors(per capita pesos of 1996)

LatitudeBands

28-32 24-28 20-24 16-20

7,555.7 9,466.2 6,479.9 9,066.9Temperature 10-18 °C 10-26 °C 18-26 °C 26°C or

more12,497.1 6,750.8 7,982.5 9,950.0

Rainfall 0-600mm

300-1000mm

300-2000mm

300-4000mm

8,592.0 7,842.9 10,000.3 5,443.8Trademobility

0-15 15-25 25-35 35 or more

7,267.7 7,662.3 4,831.3 7,148.2Source: Authors’ calculations. See Appendix.

There is no obvious North vs. South, hot vs. cold or wet vs. dry pattern, but it turns out

that the lowest GDP tends to be found in the center South of the country; temperate regions are

the richest, followed by the hottest tropical areas (which happen to be the oil-producing states of

Campeche and Tabasco). Richer regions seem to be found in places with either little rainfall, or a

moderate range; while the highest rainfall regions are the poorest. In terms of a man-made

geographic feature, which is the index of trade mobility measuring the density of highway and

2 Lacking official recent data, GDP figures come from an estimate by Díaz Cayeros (1997).

25

railroad tracks coupled with vehicle circulation, there is no specific pattern, except perhaps that

some regions in the central highlands, which are endowed with relatively good infrastructure, are

relatively poor (Puebla, Hidalgo, Guanajuato).

Gallup, Sachs and Mellinger(1999) argue that population density is one of the features

that might account for economic development, in terms of a tendency to find population

concentrating or migrating towards temperate climates, close to the sea or navigable rivers.

Population density varies greatly among Mexican states, ranging from more than 4,000

inhabitants per square kilometer in the Federal District to a little over 5 in Baja California Sur.

GDP density, calculated as GDP per square kilometer in Mexico is not distributed according to

the world patterns reported in Gallup, Sachs and Mellinger (1999), but is rather concentrated in

the central highlands of the country. The states of Mexico and Morelos, both adjacent to Mexico

City, head the list, followed by the smallest states which, except for Colima, are also in the

highlands: Aguascalientes, Tlaxcala and Queretaro. The next states are all large, but are also

characterized by concentrating economic activity: Nuevo Leon, Puebla, Jalisco, Hidalgo and

Veracruz. In the case of Mexico population density and GDP density do not seem to account for

economic growth patterns but are instead mostly the consequence of the process of centralization

of resources around Mexico City. As will be discussed futher below, econometric estimates

failed to find an explanation for GDP density other than the degree of urbanization characterizing

a particular state.

Having provided this brief sketch of geographic features, we believe it is important to

provide some quantitative sense of how important the “natural ecology” of Mexico is for the

economy. Until recently it was difficult to provide anyestimates of the way in which the

development process in Mexico is related to the natural resource base. However, INEGI has

recently produced a system of environmental national accounts (Sistema de Cuentas Económicas

y Ecológicas de México 1988-1996) which provides some insight into this specific aspect of the

interface between geography and development. We want to stress the issue of the natural

resource base and environmental degradation because, although this is not really incorporated

into the analysis that follows, we believe it is a way to quantitatively measure the impact of the

natural ecology and geography in the development process. Keeping detailed environmental

national, state and even municipal accounts should be a basic activity of government.

26

Table 2 provides the physical balances of natural resources in Mexico between 1988

and 1996 according to INEGI. The last column shows the relative change in the stocks or

flows, which is the physical environmental cost of economic growth, both from depletion of

resources and degradation of the environment. INEGI calculates this cost for 1996 at

$258,366.9 million pesos, which is around 10 percent of conventional GDP.

Table 2. Physical Natural Resource Balances (1988-1996)Resource Unit of measurement 1988 1996 Annual average

percentage changeForests Thousands cubic

meters2657 2420 -1.16

Oil Reserves Million barrels 69000 62058 -1.32WaterOverexploitation(Recharge–Extraction)

Million cubic meters -4034 -5628 3.39

Air pollution Thousand tons 26266 37523 4.56Earth pollution frommunicipal solid waste

Thousand tons 19142 31368 6.37

Water pollution Million cubic meters 16652 18415 1.27Soil erosion Thousand tons 403302 616256 5.44

Source: INEGI (1999), Cuadro 1.

It is clear from Table 2 that some of the resources that are being depleted or degraded

represent major sources of economic growth and sometimes government revenue. Oil reserves

are the most obvious example, while water and soil, subject to pollution and erosion, are less

obvious. From the percentage changes, however, one can note that municipal solid waste and soil

erosion are advancing at very high rates, which will eventually have an impact on municipal

finances and agriculture production. Since these statistics are relatively recent they do not reflect

the previous degradation of forests which occurred for decades (if not centuries, as reflected in

the previous section’s discussion of the Bajio), which in turn promotes soil erosion and reduces

water recharge.

Unfortunately, information on deforestation and depletion of mineral resources is not

available at the state level, which might make it possible to assess the impact of environmental

degradation and depletion on a state’s growth. There are some cases, though, such as oil in

Campeche and Tabasco or forests in Michoacan, where such a relationship would probably be

found.

27

The lack of state-level data notwithstanding, Table 3 provides at least some indicator of

the economic cost of the natural ecology, according to economic sector, as measured by INEGI.

Regions where an economic sector with high costs to the natural ecology is more prominent are

more likely to be basing their growth on geographic rather than other factors of production. The

table shows sectoral GDP and INEGI calculation of the Net Domestic Environmental Product

(NDEP), which is GDP minus depreciation (hence Net), minus environmental degradation and

depletion (hence environmental). The figure also reports the environmental cost and what it

represents as a percentage of GDP.

Assuming these costs are distributed relatively evenly across states, it is clear that those

states whose GDP relies most on mining or on electricity, gas and water are those whose growth

is most affected by environmental degradation. Chiapas would be the most clear example of this.

States more oriented towards manufacturing and services would incur less costs, although costs

in transport, storage and communications are probably highly correlated with manufacturing and

services. Nuevo Leon would provide an example of such a state.

Table 3. Net Domestic Environmental Product by Sector, 1995

SectorGrossDomesticProduct(GDP)

Net DomesticEnvironmentalProduct (NDEP)

Depletion andDegradationCosts

Depletion andDegradationCosts asPercentage ofGrossDomesticProduct

Agriculture, forestry andfishing

91,899.3 49,464.9 22,429.9 24.5

Mining 29,071.5 10,449.9 9,782.0 33.7Manufacturing Industry 350,155.6 276,357.9 6,454.8 1.8Construction 68,358.1 55,977.0 75.4 0.2Electricity, Gas and Water 21,331.4 6,962.6 7,160.2 33.6Commerce, Restaurants andHotels

351,744.6 336,957.7 0 0

Transportation, Storage andCommunications

168,082.9 30,362.4 121,448.3 72.2

Financial Services,Insurance and Real Estate;Social, Communal andPersonal Services

598,191.4 503,346.4 30,763.3 5.1

Total 1,678,834.8 1,428,063.2 198,113.8 11.8 Source: Authors’ calculations, based on INEGI (1999), Cuadros 4-12.

28

Further research would be needed to disaggregate INEGI information by state in order to

calculate NDEP by state, and hence better understand the dynamics of growth as related to

deterioration and depletion of the environmental resource base. Furthermore, it would be

important to calculate what percentage of government revenues are accounted for by the loss of

the natural ecology. However, this first analysis of the data tells us that there might be a very

strong relationship between the exploitation of the natural resource base and the process of

development (or underdevelopment, if there is such a thing as a vicious circle of poverty and

environmental degradation, as suggested by Chiapas).

We now turn to whether there are significant relationships between location, climate and

other geographic factors and the process of development in Mexico from a state cross-sectional

point of view.

5. The Effects of Geography on Income, Growth and Poverty: A RegressionAnalysis

Does geography influence income, growth and poverty? This section tests some hypotheses for

the correlates of geography with development in Mexico. The main findings suggest that,

although the role of geography is limited, a fair amount of regional inequality in Mexico is

attributable to natural conditions and the social and political environment that reinforces such

natural conditions. Moreover, since poverty is concentrated in risky environments, where

geographic conditions are most precarious, the findings suggest that man-made changes in the

environment might make living conditions less precarious (such as building a highway in order

to improve mobility or introducing health facilities in humid tropical zones prone to infectious

diseases). We measure development through four indicators: official INEGI per capita state GDP

for 1993 and 1980, GDP growth between 1950 and 1993, and poverty in 1995, measured as the

Foster-Greer-Thorbecke (FGT) index with a poverty line set at two minimum wages.

These indicators attempt to measure the level of development in a traditional closed

economy relying on natural resource growth during the oil boom (1980 GDP), the patterns of

regional income disparity in the midst of the economic transformation from a closed to an open

economy (GDP in 1993), a long term indicator of household well-being that can reflect long term

effects of regional inequality (moderate poverty levels in 1990), and the differential pace of

modernization in different regions across the country (growth). Growth performance is quite

29

distinct from the first three indicators, since it captures a dynamic aspect of development, while

the indicators of development levels reveal cross-sectional variation among regions in Mexico.

As one could expect, controlling for other relevant variables, including the initial level of GDP,

this is the indicator that is least explained by geographic features.

These development indicators are regressed using geographic independent variables such

as average temperature and rainfall, kilometers of coastline, population density, and the number

of political subdivisions in the state as expressed in municipalities, which we believe is a proxy

for geographic fragmentation. Throughout, we control for sociodemographic changes, as

reflected in literacy levels and urbanization, in order to separate the effects of the “natural”

environment from a conventional modernization account of regional disparities. The section ends

by performing an exercise that decomposes regional inequality, measured through the Gini

coefficient, in terms of the determinants produced by the regression coefficients. Climatological

features are calculated from INEGI data, using averages for time periods that differ in each state,

but usually range from 30 to 40 years, averaged over the weather stations located in the state.

Economic data come from INEGI, while the poverty index was calculated by CIDAC from

census data.3

The level of aggregation of the data is relatively high at the state level. This limits both

the scope of the inferences and the confidence in the statitistical results. The degrees of freedom

are given by the number of Mexican states (31, minus three missing data states in the Yucatan

peninsula, for which climatic data was unreliable). However, under different specifications,

geographic variables remained important determinants of regional disparities. While a more

detailed study would need to work at the municipality, locality or even AGEB (basic

geostatistical units defined by INEGI) level, our findings suggest that the geographic differences

in Mexico are stark enough so as to uncover some regularities even at this, admittedly high, level

of analysis. All estimates are Ordinary Least Squares regressions with standard errors corrected

for heteroskedasticity.

3 It was impossible to locate reliable climatic data for the states of Campeche, Quintana Roo and Yucatan, so theyare excluded from this analysis.

30

a) State GDP Levels in 1993

Table 4 presents a first set of estimates for the geographic determinants of the level of per capita

GDP in the Mexican states in 1993, measured in natural logs (LGDP93). The first equation

includes the independent variables of climate and location, given by the average rainfall and

temperature as measured in the meteorological stations found in each state over the last 30 years;

kilometers of coastline; population density (measured as inhabitants/km); and a dummy variable,

BORDER, for a state on the U.S. border.4 The functional form for rainfall and temperature is

quadratic, since a graphical inspection of the data reveals a non-linear relationship, whereupon

both high and low levels of rainfall are associated with higher income levels.5

Table 4. Geographic Determinants of State GDP Levels, 1993Dependent Variable: LPIB93

(1) (2) (3) (4) (5)Cons 4.054627

(1.625)3.783938(10.216)

2.703537(8.94)

3.179507(-0.18)

1.581167(1.678)

Rainavg -0.00068(-1.243)

-0.00084(-1.569)

-0.00033(-1.068)

-6.3E-05(0.537)

.0004818(0.982)

Rain2 1.25E-07(0.591)

2.34E-07(1.213)

1.48E-07(1.353)

6.16E-08(0.567)

-1.58e-07(0.886)

Tempavg -0.08377(-0.356)

Temp2 0.002895(0.511)

Coast 7.07E-05(0.588)

Border 0.432294(2.533)

0.392953(2.415)

0.018401(0.161)

0.087662(0.851)

.0914926(0.568)

Ln Density 0.206533(3.372)

0.168632(2.393)

0.02822(0.465)

0.054196(2.212)

.1484329(1.757)

Urban 1.953585(5.325)

1.222225(2.218)

1.077618(2.066)

Femill -1.826(9.718)

-1.473351(-2.034)

Ln Mun/km -0.150266(-1.720)

F 4.08 6.55 13.32 19.96 11.87

R2 0.4349 0.4964 0.7073 0.7416 0.7877 T-statistics in parenthesis, n=29.

Rainfall and temperature seem to behave according to a U-shaped pattern, in terms of the

signs of each variable, but they fail to reach statistical significance. Among the location

variables, the border dummy has a powerful effect on income levels. The coefficients suggest

4 The Appendix shows the effect of using the variable LANDLOCK for a state having no connection with thecoastline or the borders5 Unreported estimates were also carried out with various measures of dispersion (standard deviation, coefficient ofvariation) and the median of the temperature and rainfall variables, but they failed to reach statistical significance.

31

that the border location increases income by 0.43. Given the range of variation in the dependent

variable (which goes form 1.5 to 2.2), this implies that, ceteris paribus, if a state is located on the

border it will not fall into the poorest 20 percent of the distribution of Mexican states. When

population density and urbanization are taken into account, as in equations 3-5, the border is no

longer a determinant of income. The COAST variable is positive but not significant, suggesting

that access to the coast might be associated with higher incomes. However, this variable fails to

reach significance under almost all specifications, as shown in the Appendix.

Table 5.1 in the Appendix shows shows that results for equation 1 hold when the COAST

variable is dropped, and that the effects remain when BORDER is substituted for the dummy

variable LANDLOCK. The signs suggest that landlocked states would have lower incomes,

although the effect is smaller than with border states, and the significance of this variable is

suspect. In fact, what is probably happening is that landlocked states only seem to be poorer

because they are not on the border, and the really important location variable is the border with

the U.S. Table 5.1 also shows that effects (and sizes of the coefficients) remain basically

unchanged in a specification only containing the variables that are sometimes significant, namely

rainfall (and its square), border and population density. This specification provides the baseline

regression, reproduced in Table 4 as equation 2, to which we incorporate urbanization effects,

quality of infrastructure, human capital, and finally the number of municipalities, which we

believe is a central determinant of income levels due to a historical lock-in.

The first human modification to the natural environment is created by cities and their

accompanying public goods. In cities radical transformation of the natural ecology take place,

when water, sewage or electricity are introduced. Equation 3 provides an estimate of

urbanization effects on per capita GDP, controlling for geographic variables in the baseline

regression. The sign of the urbanization variable, defined as the percentage population living in

localities with more than 5,000 inhabitants, is positive, which suggests the higher the income, the

more urbanized the state. Such a result is hardly surprising. The difficulty with this finding is

understanding which aspect of urbanization is relevant to generating higher incomes.6

6 Given the extremely high significance of the urbanization and land density variables, an additional test suggestedby John Gallup would be to find out whether geographic variables, particularly those related to climate and location,explain GDP density, which is the product generated per square kilometer. We have found, however, that GDPdensity in Mexico is randomly distributed in terms of climate and location; although it is higher where population ismore concentrated, which in Mexico happens to be the central highlands.

32

As Table 5 shows, urbanization is highly correlated with measures of public good supply

(percentage of homes with electricity, water and sewage) and human capital (such as total adult

illiteracy, female illiteracy or the percentage of the population which speaks only an indigenous

language). Given the multicollinearity problem involved in putting all these variables in the same

estimate, we provide other estimates in Table 5.2 in the Appendix, using specifications which

provide the same basic message: these variables are always significant and with the correct signs,

in terms of better infrastructure being found in the richer states. The causality of the second

observation is obviously complex, since it could well be that those regions have better services

precisely because they are rich (this issue is further discussed below).

Table 5. Correlation of Infrastructure and Human Capital VariablesElect Agua Dren Urban Illite

Elect 1Agua 0.8407 1Dren 0.7332 0.7096 1Urban 0.7516 0.7852 0.7234 1Illite -0.7245 -0.8095 -0.7468 -0.7621 1Illfem -0.7243 -0.8097 -0.7295 -0.7413 0.9941

Table 5.3 in the Appendix provides estimates of the effects of human capital, measured

by female illiteracy (FEMILL) and percentage of the population that only speaks and indigenous

language (LENGUA).7 Equation 4 shows that when the human capital variables are introduced,

the explained variance increases to almost 74.16 percent, slightly above the infrastructure

variables. Nonetheless, while geographic variables alone accounted for 43 percent of the

variance in income levels across Mexican states, an improvement to around 71 percent is

achieved with the infrastructure variables.8

A last set of income level regressions, found in Tables 5.4 and 5.5 in the Appendix, deals

with the number of municipalities in each state. We believe the number of municipalities is a

good proxy for a geographic component, namely the division of the territory into valleys

delimited by mountainous natural boundaries, combined with the role of political jurisdictions

7 Estimates (unreported) were also done with an index of human capital measured as primary and secondary schoolenrollment and with total adult illiteracy, which yielded the same basic results.8 Unreported regressions of the income levels only with the urbanization variable and one of the quality ofinfrastructure variables, excluding geographic determinants, account for around 30 percent of the variance, whichseems consonant with this improvement.

33

and a historical lock-in effect of rulers attempting to produce internal borders in order to prevent

exit. Such internal borders, we believe, limit trade and enhance the predatory power of the ruler,

thus hindering growth and development.

The specification of the variable “number of municipalities” was tried by using first the

absolute number (MUNIC), then municipalities per capita (MUNPC), and finally the number per

square kilometer (MUNKM). In all cases the geographic variables and urbanization were kept in

the regression, plus a control dealing with indigenous populations or female illiteracy, both of

which are highly correlated (ρ=0.8435). The reason to include the human capital control in the

version that most reflects the indigenous component is that we want to ensure that the municipal

variable is capturing something other than the fact that states with more indigenous population

(namely, Oaxaca, Chiapas and Puebla) also have more municipalities.

The number of municipalities in the different measurements always had a negative effect

on income levels and was mostly significant. The exceptions occurred in cases where female

illiteracy was included as a control; this probably reflects the fact that the most isolated

communities, with female illiteracy rates as high as 90 percent at the municipal level, are found

precisely in the rugged states with the most municipalities. The correlation between the variables

is relatively high (p=0.5662). The fit of the regressions improved as the number of municipalities

was adjusted first by population and then by territory. In fact, the variance accounted for by

incorporating number of municipalities went all the way to 79 percent.

An issue that must be addressed regarding the municipal variable is whether there is an

endogeneity issue generated by a notion that regions with higher population growth and that are

more densely populated might become impoverished due to degradation of the natural resource

base. It could also be considered that the fragmentation of political jurisdictions could be the

consequence of low income levels, rather than a cause. In order to test for this possibility, Table

5.5 in the Appendix reports estimates of GDP levels in 1993, controlling for geography,

urbanization and human capital, with the lagged value of the municipal variable. This lagged

value is taken back to its origin circa 1825, when municipalities were organized across the

country, first as a consequence of Spain’s Cadiz Constitution in Spain and then after

34

independence.9 The lagged effect of the municipal variable is always negative and significant,

yielding results very similar those found for present-day municipalities.

The above results suggest that population density and urbanization are the variables that

exhibit the most significant and robust relationships of all, where more densely populated urban

regions are richer. This could be the result of the natural endowments of those regions, but it is

also clear that higher income represents a clear incentive for migration into those regions. In

contrast to the Gallup, Sachs and Mellinger (1999) findings at the world level, the densely

populated regions in Mexico are in the highlands, far from the coast or major waterways. Some

of the more densely populated states are also relatively small, which suggests that another reason

may underlie their performance: being small may have some advantage for development. State

size measured by population was included in unreported estimates, but it failed to be significant

once density was taken into account. Although this result would seem to undermine the

importance of natural geography for development, the results in the next series of estimates, with

indicators including GDP in 1980, growth and a poverty index, suggest that geography had

become less important by the end of the twentieth century.

Moreover, other unreported estimates of the effect of geography on municipal revenue

suggest that at a lower level of aggregation, rainfall and temperature might play a greater role

than at the state level.10 While those other estimates are not for per capita GDP, since no such

statistics exist at the municipal level, revenue collected by municipal taxes is probably highly

correlated with the resource base and income of a given location. It is important to note that

although further research is necessary, natural geography remains an important determinant of

tax collection controlling for welfare levels as defined by an index of satisfaction of basic public

good provision in the municipality.

Before turning our attention to estimates of the effect of geography on poverty, growth

and income levels in 1980, an additional issue that we address is whether there is a relationship

between the previous results and the land types that characterize each state. In particular, Table 6

provides estimates of the effect of land types on 1993 GDP measured as the percentage of a state

territory constituting land suitable for agriculture, grazeland, forest, bush, rainforest, and other 9 The series was constructed based on Hernández Chávez (1993), Tables 1 and 2, and attributing municipalities tothe states that were later formed out of the provinces of Nueva Vizcaya and San Luis Potosí with the maps inArchivo General de la Nación (1996).

35

types of land. The results suggest that agricultural land might have a negative effect on

development, while the bush characterizing much of the North might have a positive effect. The

significance of the other land types variable is mostly related to the high GDP of Tabasco, which

is also characterized by a unique type of marshland.

Table 6. Land Types and State GDP Levels in 1997(1) (2) (3) (4) (5) (6)

Rainavg -0.0000292(-0.102)

0.0003359(0.712)

0.0003484(0.734)

0.0009068(1.965)

0.0002788(0.667)

0.0009808(1.356)

Rain2 4.81E-09(0.04)

-1.17E-07(-0.63)

-1.22E-07(-0.669)

-2.79E-07(-1.688)

-8.66E-08(-0.54)

-3.19E-07(-1.299)

Border -0.0631508(-0.587)

0.0363042(0.249)

0.0367881(0.274)

-0.0593565(-0.497)

0.0463352(0.381)

-0.0244796(-0.178)

Lndens 0.1362684(2.682)

0.1386411(1.574)

0.138232(1.536)

0.1384746(1.714)

0.0594675(0.832)

0.1030983(1.303)

Urban 1.648799(4.076)

1.639327(3.536)

1.629437(3.895)

1.789713(5.742)

1.545269(3.677)

1.899039(4.102)

Lnmunkm -0.0732613(-1.495)

-0.1674797(-1.896)

-0.1674427(-1.9)

-0.1573938(-2.307)

-0.1065821(-1.395)

-0.1924887(-2.171)

Agric -0.007983(-4.755)

Graze 0.0002387(0.041)

Forest -0.000317(-0.112)

Bush 0.0056068(2.452)

Other 0.0101982(3.467)

Rainfore -0.0056079(-1.299)

_cons 2.083231(4.152)

1.020182(1.029)

1.03071(1.08)

0.5423051(0.641)

1.787259(2.31)

0.561887(0.519)

F 18.88 8.04 8.01 13.38 65.1 9.09R2 0.8373 0.766 0.7661 0.8022 0.8162 0.7843

A final possibility, tested in Table 7 is that what really matters is not exactly the land

type, but the homogeneity of land characterizing a particular state. To test this we construct a

land type fractionalization index, which is simply the Hirschman-Herfindahl index, with the land

types of each state (grazeland, forest, bush, rainforest and other). The fractionalization index fails

to be significant, although further research could well find other alternatives to measure the

degree of land homogeneity. Table 7 also shows that a major feature of agriculture, the

organization of a state in ejidos (the traditional communal land tenure system discussed in the

historical section), correlates negatively with development, consonant with the negative sign

previously found for agriculture in equation 1 of Table 6. However, as discussed at the end of

10 We thank Eduardo Lora for pointing out the relevance of these findings for the discussion of state-levelregressions.

36

this section, such a finding is probably more related to political jurisdictions than to natural

geography.

Table 7. Fractionalization of Land Types and Ejido Organizations for AgriculturalProduction as Determinants of 1993 GDP

(1) (2)Rainavg 0.0001968

(0.529)0.0005764(1.433)

Rain2 -6.95E-08(-0.491)

-2.01E-07(-1.291)

Border 0.0170378(0.119)

0.0359733(0.32)

Lndens 0.1320696(1.529)

0.1957718(2.834)

Urban 1.72082(3.881)

1.340769(3.122)

Lnmunkm -0.1551498(-1.956)

-0.1329006(-2.208)

Fracc -0.0217427(-0.649)

Lnejidkm -0.1769077(-2.771)

_cons 1.230453(1.408)

0.3873119(0.515)

F 7.95 11.07R2 0.7694 0.8044

b) Poverty Levels in 1990

An alternative way to measure state development is not through GDP, but rather through poverty

indicators. The Appendix provides a full set of tables (Tables 5.6 through 5.9) which assess the

effect of geography on poverty levels measured through the FGT index calculated for 1990. The

FGT index constitutes an indirect poverty measure, which is based on the reported income of a

family according to census data. While more reliable income data from households surveys

exists, it is not possible to disaggregate a poverty index at the state level from that source. Hence,

the FGT index was calculated setting the poverty line at two minimum wages, and establishing in

the index a concern for the distribution of poverty by making its parameter α=2. It must be

stressed that the dependent variable is not a welfare index that includes the infrastructure and

urbanization variables as part of its components (such as INEGI’s indice de bienestar or

CONAPO’s indice de marginación), but an indirect poverty index measured through family

earnings.

37

As shown in Table 8, the basic finding is that natural geography variables are not robust,

since quality of infrastructure and human capital account for most of the variance in poverty

levels. Although at first sight location and population density seem to be important for poverty

levels, once urbanization is taken into account, with all the public good provision issues it

encompasses, those variables fail to reach significance. The bottom line in this respect is that

poverty is concentrated in rural areas, so that urbanization almost uniquely determines poverty

levels. However, a more crucial result is that the municipal variable remains important and

significant for the determination of poverty levels.

Table 8. Geographic Determinants of State Poverty Levels 1990Dependent Variable: FGT(2,2)

(1) (2) (3) (4) (5)Cons .168338

(0.287).521036(7.863)

.372475(3.462)

.4522231(5.571)

.402986(3.656)

Rainavg .0001752(1.151)

.0000887(1.488)

-.0000103(-0.187)

2.77e-06(0.064)

-.0000174(-0.352)

Rain2 -2.09e-07(-0.370)

-2.76e-08(-0.989)

3.26e-09(0.163)

4.98e-10(0.033)

8.56e-09(0.439)

Tempavg .0084413(0.143)

Temp2 -.0005194(-0.359)

Coast 1.23e-06(0.048)

Border -.0735318(-2.869)

.036904(1.423)

.021144(0.976)

.0151645(0.646)

.029022(1.306)

Density -.0003827(-2.952)

.0001002(1.301)

.0000496(0.891)

-.0000149(-0.311)

-.0000392(-0.560)

Urban -.550246(-5.629)

-.348977(-2.706)

-.409791(-3.876)

-.3848575(-2.870)

Femill .562566(2.740)

1.60672(4.783)

Language .443549(2.219)

Mun / km 7.507978(6.650)

7.14284(4.707)

F 11.57 14.12 23.51 87.30 20.99R2 0.6337 0.7921 0.8568 0.9076 0.8915

An additional aspect that should be stressed is that, although the number of municipalities

is greatest in states with a strong indigenous population, such as Oaxaca, Chiapas and Puebla,

controlling for indigenous population with the variable LANGUAGE, which is the percentage of

people in a state that only speak an indigenous tongue, yields still a highly significant effect of

38

political framentation on poverty. More fragmented states tend to be poorer, once we have

controlled for public good provision, human capital formation or ethnic conditions.11

c) Growth of GDP Regressions from 1950 to 1993

In terms of the geographic determinants of GDP growth, another set of tables in the Appendix

(5.10 through 5.13) incorporate first pure geographic variables of climate, location and

population density, then urbanization effects, followed by quality of infrastructure, human

capital, and finally the number of municipalities.

Table 9 below reproduces the most important findings. In the growth regressions, all

estimates are done controlling for the initial income levels, namely, state GDP per capita in 1950.

Convergence is observed in terms of the negative and highly significant coefficient of this

variable, reflecting that initially poor states have tended to grow faster than rich ones.

Conditioning variables for this convergence are added in the spirit of the new literature on

growth so that, instead of controlling for human capital through female illiteracy as in the

previous estimates, we use the percentage of children enrolled in elementary school in 1959.12 It

is noteworthy that among the geographic variables, population density and rainfall always

survive as determinats of growth; location on the border or along the coastline is no longer

significant. Quite surprisingly, urbanization in 1960 is not a good predictor of economic growth,

regardless of its importance in the previous estimates.

11 One possible avenue for further research would be to use GIS data at the AGEB (Area Geoestadística Básica)level of rainfall, location, urbanization, population density, illiteracy, language and even land use, available fromINEGI, combined with the newly released NIBA database, which provides poverty indicators also by AGEB.12 The Appendix shows that using 1940 instead of 1959 yields basically the same result. Education has not been aconditioning factor in the way found in much of the growth literature, although this might be to some extent a matterof measurement, since secondary school enrollment tends to be significant in estimates.

39

Table 9. Geographic Determinants of State Growth, 1950-1993Dependent Variable: growth5093

(1) (2) (3) (4) (5)_cons 3.853566

(2.605)3.013644(10.749)

3.026254(11.084)

2.91656(10.374)

2.943211(5.863)

lnpib50 -0.5488505(-7.199)

-0.5454843(-9.572)

-0.5669299(-6.791)

-0.6031166(-7.306)

-0.6000654(-5.247)

Rainavg -0.0014015(-3.664)

-0.0013783(-4.442)

-0.0013487(-3.718)

-0.0014186(-4.145)

-0.0014283(-3.933)

rain2 6.81E-07(5.38)

6.95E-07(6.214)

6.88E-07(5.523)

7.12E-07(6.418)

7.16E-07(6.013)

Tempavg -0.0897579(-0.617)

Temp2 0.0024662(0.732)

Border -0.0088027(-0.068)

Coast -0.0001007(-1.178)

-0.0000805(-1.061)

-0.0000743(-0.89)

-0.0000908(-1.234)

-0.0000916(-1.196)

lndens50 0.0877558(2.368)

0.0886773(4.285)

0.082908(2.536)

0.0846422(2.675)

0.082734(1.785)

urb60 0.0979985(0.278)

-0.0042092(-0.012)

-0.009359(-0.024)

iprim59 0.4704316(1.562)

0.4708052(1.53)

Ln mun / km 0.0033509(0.055)

F 78.14 59.91 46.42 87.11 73.22R2 0.8979 0.8914 0.8918 0.9002 0.9002

When controlling for all these variables, the variables for fragmentation of political

jurisdictions was no longer significant. This result suggests that although initial levels of GDP in

1950 are determined by the number of jurisdictions, such a political variable does not have

effects on the margin for changes of income through time. Although further research would be

needed in order to prove this assertion, and maybe a two-state estimation procedure is called for,

this is an encouraging result for the future prospects of development in the Mexican states.

Growth regressions were also tested according to land use (according to INEGI) depending on

whether land was unsuitable for agriculture, and land types classified by agricultural, bush, forest

or rainforest. No significant results were obtained, but this could be a consequence of the level of

aggregation.

40

d) GDP Levels in 1980

A relatively surprising finding in the course of this research was that geographic variables were

stronger determinants of GDP in 1980 than in 1993. Such a result was also found for regressions

carried out with GDP figures for 1950, 1960 and 1970. This might suggest that depending of the

makeup of an economy and the type of specialization that characterizes it geography might be

more or less important. In 1980 the Mexican economy was heavily reliant on oil, little diversified

in its exports, and still heavily weighted by a history of an import substitution industrialization

strategy. By 1993 radical changes had already occured in terms of a greater reliance on

manufacturing exports and an insertion into the world economy which soon would become

firmly embedded in the institutional framework of NAFTA. In a way, Mexico was a very

different country in 1980 than in 1993; in 1980 geographic variables played a stronger role.

Table 10. Geographic Determinants of State GDP Levels, 1980Dependent Variable: LPIB80

(1) (2) (3) (4) (5)Cons 2.4629

(1.0773)1.88822(18.282)

1.37908(11.306)

1.556826(9.516)

1.422119(8.999)

Rainavg -.000535(-2.497)

-.0005814(-3.220)

-.0005331(-4.551)

-.0003813(-2.788)

-.0003435(-2.940)

Rain2 2.63e-07(3.223)

2.49e-07(4.217)

3.09e-07(6.795)

2.62e-07(5.640)

2.45e-07(5.948)

Tempavg -.06681(-0.621)

Temp2 .001831(0.707)

Coast 3.85e-06(0.086)

Border .179146(2.734)

.168723(2.775)

.056026(1.098)

.068052(1.287)

.029569(0.669)

Density .0001161(5.868)

.0001169(12.281)

.0000551(4.043)

.0000554(4.293)

.0000665(7.054)

Urban .694243(4.387)

.454746(2.191)

.629276(3.141)

Femill -.771651(-2.965)

-.48519(-1.869)

Mun/km -13.76061(-4.812)

F 26.33 38.48 102.20 150.72 185.03R2 0.7049 0.6782 0.8422 0.8740 0.9119

41

As Table 10 shows, in 1980 the findings are similar to those for levels in 1993, but there

is now a very strong effect of the rainfall variable, which was not significant before. All the

equations reveal a highly significant relationship with rain, where the negative sign of RAIN

involves a lower income level for states with higher rainfall, but such a relationship reaches its

bottom at the intermediate levels of rainfall, as can be seen in Figure 5, which uses the

coefficients of this first equation to simulate the effect of rainfall. Thus in 1980 rainfall was

associated with lower income, particularly for states in the 1,000 mm range (Colima, Guerrero,

Hidalgo, Mexico, Morelos, Nayarit, Oaxaca and Puebla) but this effect was not important for the

extreme cases of states like Baja California (151 mm of rainfall) or Tabasco (2,297 mm).

Figure 5.

EFFECT OF AVERAGE RAINFALL ON PER CAPITA GDP

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0

200

400

600

800

1000

1200

1400

1600

1800

2000

average rainfall

LOG

GD

P

The significance of this finding is that trade liberalization, and in general economic

openness, seems to be an important escape rout from natural geographic handicaps. A region that

is not well endowed in terms of rainfall and location variables would seem to do worse under a

closed than an open economy, as suggested by the comparison of the 1980 and the 1993 results.

Moreover, these estimates seem to suggest that the geography of public good provision is a much

stronger and more relevant determinant of income levels in open than closed economies.13

13 We owe this insight to Eduardo Lora. Further research should be pursued along the lines of the effect of tradeopenness on geographic determinants of income.

42

e) Regional Inequality Decomposed

Table 11 provides an estimate of GINI coefficients for simulations for inequality if the deviations

of the independent variables were subtracted from their average value. That is, the exercise tells

us how much regional inequality can be attributed to the specific inequality produced in the

regression by the variance in each specific independent variable. The exercise is carried out

incrementally, so that the first variable which is discounted is rainfall, to obtain a predicted

dependent variable (GDP levels, growth or the poverty index), and then a GINI index to capture

the overall distribution, without the rainfall effects. The next line in the table generates a new

GINI coefficient for the previous predicted value subtracting the border effect, then population

density, and so forth. Hence the last line of each estimate represents, in fact, a residual inequality

that is not accounted for when the variances of all the independent variables have been

eliminated.

Table 11. GINI Coefficients Isolating Effects of Explanatory Variables(Cumulative effects)

LGDP80 LGDP93 GROWTH FGT22Dependent variable (Y) 0.2605 0.2834 0.2142 0.2285- averagerainfall

0.2798 0.2955 0.2034 0.2281

- border(dummy)

0.2774 0.2896 0.2491

- coastline(km)

0.1984

- density(pop/km2)

0.1813 0.2339 0.2093 0.2518

- urbanization 0.0876 0.1726 0.2012 0.1435

- femaleilliteracy

0.0704 0.1352 0.1051

- primary schoolenrollment

0.2013

- political jurisdictions(municipalities/km)

0.0559 0.1129 0.0807

- initial GDP 0.0884

43

Table 12 now provides the percentage of inequality which is cumulatively produced by

“neutralizing” each of the independent variables on top of the previous one.

Table 12. Relative Contribution to Inequality Measured by GINI Index(Cumulative effects, as percentage of dependent variable GINI)

LGDP80 LGDP93 GROWTH FGT22Rain -7.4 -4.3 5.0 0.2Border -0.9 2.1 -9.2Coast 2.3Density 36.9 19.7 -5.1 47.4Urban 36.0 21.6 3.8Illiteracy 6.6 13.2 16.8School 0.0Municipal 5.6 7.9 10.7Initialgdp 52.7Unaccounted 21.5 39.8 41.3 35.5

It is clear that the most prominent contribution to income inequality is made by

urbanization, which is highly correlated with all the public good variables (water, electricity,

sewage). When the urbanization effect is discounted, the GINI coefficient is smaller. This means

that the distribution of public goods in Mexico has actually enhanced regional inequality.

Although there has been a tendency for some convergence in the provision of public goods

across states throughout the whole century, this means that the policies that have provided those

goods have, nonetheless, generated greater regional inequality. It is important to note that this

effect remains controlling for density, which is the other variable that contributes most to

inequality, which means that such an effect of urbanization is related to public goods, not just to

the mobility of population through migration towards more attractive geographic locations.

Average rainfall has the opposite effect, in that when the rainfall effect is neutralized, inequality

increases. Such an effect, is however, relatively small.

6. The Meaning of Municipalities

A question arises as to the precise meaning of the municipalities variable. One could argue that

the number of jurisdictions is unrelated to geography, but produced by policy variables or an

optimal size given by economic processes. Moreover, there is an issue as to whether an

endogeneity problem exists in the previous findings. The remarkable aspect of this variable is

that the number of municipalities has been quite constant during the last two centuries. Tables 13

44

and 14 show the evolution of municipalites in Mexico. The first table provides estimates for the

number of municipalities by state in 1825, 1917 and today. The second provides more detailed

information concerning municipalities in 1917 and the information that exists on what

institutional arrangement immediately preceding them in the nineteenth century.

Table 13. Number of Municipalities by State1825 1917 1999

Aguascalientes 1 8 11Baja California 5Baja California Sur

5

Campeche 1 8 9Coahuila 4 37 38Colima 1 8 10Chiapas 59 111Chihuahua 2 64 67Distrito Federal 1 13 16Durango 2 43 39Guanajuato 14 33 46Guerrero 33 67 76Hidalgo 63 84Jalisco 15 124México 72 118 122Michoacán 90 75 113Morelos 17 33Nayarit 1 17 20Nuevo León 7 49 51Oaxaca 200 570Puebla 172 142 217Querétaro 6 18Quintana Roo 18 8San Luis Potosí 10 55 58Sinaloa 16 18Sonora 76 70Tabasco 17 17Tamaulipas 7 37 43Tlaxcala 42 44 60Veracruz 6 183 207Yucatán 160 106Zacatecas 3 50 56

2,749 3,160 4,427

Sources: For 1825, Hernández Chávez (1993) in bold type and Secretaría de Gobernación (1996) in italics;for 1917 INEGI (1997); for 1999, Cedemun (1999).

45

Table 14. Evolution of Territorial Division from the Late Nineteenth Centuryto the Twentieth Century

Municipalities before 1917 Municipalities in 1917Aguascalientes (18/10/1868) divided into 4 partidos 8 municipios libres

Baja California n.d n.a.

Baja California Sur n.d n.a

Campeche (13/07/1877) divided into 5 partidos 8 municipios libres

Coahuila (20/12/1868) divided into 5 distritos 6 distritos judiciales and 37 municipios libres

Colima (13/07/1867) 2 partidos and 6 municipalidades 8 municipios libres

Chiapas (05/02/1857) divided into 7 departamentos 59 municipios libres

Chihuahua (1867) 5 distritos and 17 cantones 64 municipios libres

Distrito Federal (05/02/1857) 1 municipalidad and 4 partidos 13 municipalidades

Durango (1867) divided into 13 partidos 43 municipios libres

Guanajuato (05/02/1857) 5 deptos and 20 municipalidades 33 municipios libres

Guerrero (18/05/1847) 4 distritos 15 distritos and 67 municipios libres

Hidalgo (16/01/1869) 11 distritos n.a.

Jalisco (30/12/1836) 8 dsitritos and 19 partidos (22/08/1864) 8 cantones and 28 departamentos.

México (14/10/1870) 16 distritos 118 municipios libres

Michoacán (1867) 17 distritos, 75 municipalidades, 216 tenencias (24/04/1868) 17 distritos, 75 municipalidades, 216 tenencias

Morelos (17/04/1869) 5 distritos n.a.

Nayarit (12/12/1884) 6 partidos 17 municipios libres

Nuevo León (22/08/1846) 3 distritos, 7 partidos, 31 municipalidades 49 municipalidades and 1 congregación

Oaxaca (05/02/1857) 25 distritos and 22 partidos 50 municipal departments

Puebla (14/12/1853) divided into 24 partidos (1867) 18 distritos and 142 municipalidades

Querétaro (18/01/1869) 6 dsitritos 6 municipios libres

Quintana Roo (24/11/1902) 2 prefecturas n.a.

San Luis Potosí (24/05/1869) 3 partidos and 52 municipalidades 55 municipios libres

Sinaloa (03/03/1865) 2 departamentos 16 municipios libres

Sonora (14/05/1869) 9 distritos 76 municipios libres

Tabasco (18/12/1883) 17 municipalidades 17 municipalidades

Tamaulipas (05/02/1857) 3 dsitritos and 11 partidos 37 municipios libres

Tlaxcala (03/03/1865) 5 dsitritos and 27 municipalidades 44 municipios

Veracruz (05/02/1857) 18 cantones 183 municipios libres

Yucatán (05/02/1857) 13 partidos n.a.

Zacatecas (1867) 12 partidos 50 municipalidadesSource: INEGI (1997).

46

Figure 6 shows just how resilient political jurisdictions in Mexico seem to be, in that for

those states where data is readily available, the correlation between municipalities in 1917 and

1999 is very high.

Figure 6.

Resilient Political Jurisdictions

0

50

100

150

200

250

0 50 100 150 200 250

Number of municipalities 1917

Num

ber o

f mun

icip

aliti

es 1

999

Finally, Table 15 reports that the number of municipalities in 1825 is a very good

predictor of municipalities 170 years later, particularly if controls are introduced for population

at the turn of the last century (POB1893) and the territory measured in square kilometers. Table

15 also reports that climate and location variables do not explain the number of municipalities,

when the initial number is controlled for. However, as the last estimate in the table shows, when

the initial number of municipalities is not included in the estimation, the variables of rainfall and

coastline are important determinants of number of municipalities. More jurisdictions exist in the

coast and at middle ranges of rainfall. Further exploration with topographical measures might

reveal that the number of municipalities is quite geographically determined, although we believe

that the basic thrust of this variable is political. Municipalities can be taken as a useful proxy of a

historical lock-in phenomenon that incorporates geography and political incentives, and the

numbers should be taken seriously as a determinant of income levels.

47

Table 15. Historical Origin of Number of MunicipalitiesDependent Variable: Munic

(1) (2) (3) (4) (5) (6)Cons 33.22175

(3.053)-20.9455(1.205)

-6.425667(-0.367)

-26.05414(-1.2255)

-362.4151(-1.503)

-629.4326(-1.060)

Mun1825 1.491698(2.682)

1.3168(2.335)

1.28880(2.316)

1.29608(2.308)

1.538409(2.545)

Km2 0.00044(2.373)

.0003252(2.050)

.0005186(2.168)

.0005451(2.642)

0.0005213(1.669)

Pob1893 0.086(2.29)

.0966676(2.297)

.086208(2.028)

.038580(1.428)

0.1270358(1.988)

Border -25.1015(-.0983)

10.2253(0.389)

Coast .015848(0.870)

0.0655094(2.006)

Landlock -28.23828(-1.284)

Rainavg .100647(1.453)

0.3807909(2.044)

Rain2 -.0000185(-0.780)

-0.0001138(-1.855)

Tempavg 29.6829(1.257)

43.84365(0.873)

Temp2 -.755366(-1.318)

-1.204517(-0.991)

F 7.19 6.22 5.06 3.73 3.39 2.93R2 0.5783 0.6838 0.6972 0.6957 0.7916 0.5343

How do we understand this process to have developed? Mexico’s natural ecology shows

several distinct regions that very early affected population patterns. During pre-Hispanic times,

the available primitive technologies limited large populations to areas where sufficient food was

easily produced. This meant that those regions with stocks of water, rich soils and appropriate

temperature were first settled, while less well endowed areas remained relatively vacant. These

more favorable areas were the Michoacan and Guanajuato lakes region, the central Valley of

Mexico, the Valley of Oaxaca, small areas in the Veracruz midlands and the tropical Mayan

region. Northern Mexico was sparsely populated by nomads, while jungles and mountains were

left as refuge areas to weaker and smaller nations.

The sixteenth century Spanish conquest introduced new technologies and institutions in

Mexico. The victorious Spaniards, however, immediately became overlords and began

extracting the rents that had formerly been controlled by the dominant Indian groups. The

demographic catastrophe14 that followed the military conquest required that if rent extraction 14 The population dropped in the sixteenth century from somewhere around 20 million Indians in 1520 to barely 2million in 1600.

48

were to be continued, the Indian population had to be carefully controlled and kept in one

geograhical location. This meant that both institutions and policies had to be adapted to the new

low-density reality. Individuals, now fewer in number, became more valuable and had to be

confined to the land which they made productive. Allied Indian chieftains were given townships

and territorial jurisdictions to govern and from which extract rents. These entities maintained the

indigenous communal land tenure system and in time became the origin of most municipalities in

central Mexico. Spaniards, on the other hand, were given encomiendas (land grants) and

repartimientos de indios15 (site-specific allocations of indigenous labor in an arrangement similar

to serfdom) to make these lands productive. This practice became the origin of the large

haciendas and of new settlements in the newly opened regions. Mexico’s economic development

has thus displayed since its begining the seeds of two very different models: one based solely on

rent extraction of existing resources, in populated central Mexico, and the other based on new

technologies, stock accumulation and productivity increases in geographic regions opened by the

insertion of Mexico into the Spanish-European colonial economy, and centuries later by its

insertion into the world and global economies.

Municipalities thus developed in two very different ecological settings with different

results. Where population density was high because of propitious natural conditions for the

production of food by the available primitive technologies (the central Valleys of Mexico and

Oaxaca, the midlands of Veracruz and the lake regions of Michoacan and Guanajuato),

municipalities became efficient rent-extracting mechanisms. Where population density was low,

because of poorer natural conditions, the introduction of new food-producing technologies

(western agriculture techniques, irrigation and/or extensive cattle raising) or the discovery of

valuable mining resources, municipal institutions contributed to faster colonization, population

growth and economic development. The institutional “lock-in” phenomenon would seem to

explain the negative correlation found between the number of municipalities and economic

wealth.

Although further research is called for on this topic, one immediate question arises as to

whether some of the “lock-in” phenomena of the institutional ecology might be correlated. That

is, it could well be that the number of municipalities is correlated to the historical conformation

15 Encomiendas had the purpose of christianizing the indians by having them under the tutorship of “old Ohristians”who had to provide education and religious instruction. Repartimientos were established as rewards to the Spanishsoldiers who contributed to the conquest of Mexico by giving them whole Indian towns to exploit and benefit from.

49

of tax authority and the organization of production in agriculture as reflected by ejidos. Although

we do not attempt to probe into the possibility that all of these variables might be measuring an

underlying institutional density, Table 16 shows that this is a definite possibility as reflected by

the correlation matrix of the number of ejidos, the locations of taxes on trade in 1810

(alcabalas), and the number of municipalities in 1917, 1825 and today. The low correlation

between ejidos and municipalities in 1825 at their original inception suggests that these variables

measure different phenomena, or that at least the current number of ejidos is vary different from

what it might have been originally. The alcabala posts in 1810 show a very high correlation with

the number of all municipalities variables, which suggests that the multiplication of political

jurisdictions might be concurrent with the establishment of tax authority. However, this is mostly

speculation, since further research is necessary in order to ascertain these relations.

Table 16. Correlation Matrix of the Number of Municipalitiesand Other Institutional Density Variables

ejidos alcab810 mun1917 munic mun1825Ejidos 1alcab810 0.5074 1mun1917 0.7547 0.7537 1Munic 0.7334 0.7689 0.9592 1mun1825 0.1861 0.7892 0.5673 0.6879 1

A final important issue is whether the number of municipalities provides an explanation

for the deficient provision of local public goods in vast regions in Mexico. As Table 17 shows,

estimates using the percentage of homes with sewage, water, electricity, where indigenous

languages are spoken or the degree of female illiteracy in 1990 fail miserably to be explained by

the number of municipalities. Perhaps as much of a surprise comes from observing that initial

GDP does not provide an explanation either. We believe the problem with this estimates lies in

the level of analysis, and that a more disaggregated approach might prove to be a fruitful avenue

for further research, since it is quite likely that fragmentation of localities, for example, within a

municipality explains quite a lot of the deficiency in public good provision.

50

Table 17. Municipalities as an Explanation for Public Good ProvisionSewage Water Electricity Language Femill

Lnmunkm 0.0124444(0.327)

0.0124361(0.384)

0.0031767(0.279)

-0.0001719(-0.027)

-0.0072523(-0.32)

Lndens 0.0437953(1.487)

0.0190272(0.792)

0.0165098(1.772)

-0.0029385(-0.593)

-0.0026592(-0.157)

lnpib50 0.1101855(2.388)

0.0325018(0.806)

0.0162528(0.85)

-0.0099609(-1.27)

-0.0669561(-2.598)

Rainavg -0.0003092(-1.351)

-0.0002001(-1.218)

-0.0000902(-1.112)

0.0000427(1.163)

0.0002015(1.498)

rain2 1.09E-07(1.248)

1.78E-08(0.293)

1.44E-08(0.419)

-8.69E-09(-0.504)

-4.82E-08(-0.881)

Border -0.0448053(-0.796)

0.0007333(0.018)

-0.0019583(-0.086)

0.0102295(1.029)

0.0233623(0.679)

_cons 0.494646(1.356)

0.9255813(3.344)

0.9006226(8.963)

0.0177903(0.291)

0.1442824(0.667)

F 3.87 14.76 2.97 1.49 8.32R2 0.5046 0.6084 0.4535 0.4366 0.6794

7. Policy Implications and Research Agenda

Mexico’s past as a colonial society, based on natural resource exports, namely silver, produced

rent-seeking political institutions to serve a predatory elite that had no incentives to compete.

The oil booms in the 1920s and 1970s were not altogether so different, in that natural resource

exports did not produce an endogenous process of development. Instead, the country was

characterized by a great institutional density. This would be reflected in internal barriers to trade

which made exchange even more difficult than what the already important natural barriers

entailed. Thus Mexico has been primarily a fragmented, closed, protected economy. At the

regional level, only when transport costs declined with the arrival of the railroad, at the end of

the nineteenth century, did the North begin accelerated growth that was open and subject to

competition, and subsequently reflected in political demands. The North took advantage of the

existence of coal in northern Coahuila, and Monterrey started its industrialization. This

development can only be explained by a crucial locational factor, namely the shared border with

the United States. Geographic destiny plays a key role in the development prospects in the

country even today, as NAFTA determines much of the dynamic growth of mid-size cities in the

North of the country.

Geography has played key roles in development at various moments. The ease of

maritime transport through steamers, together with the tropical climate, allowed for the

development of plantation economies, for example in Yucatan (henequen), Veracruz (sugar) and

51

Chiapas (coffee), accompanied by the emergence of authoritarian semi-slavery political

institutions. The political and demographic center of the country specialized in incipient

manufactures and traditional foodstuffs for the national market, which was kept relatively closed

due to transport barriers. Coastlines remained relatively unpopulated due to the risk of malaria.

This pattern of regional development has long-lasting effects that are still felt today.

While the relatively young cities of the North have become thoroughly integrated into global

production and trade networks, creating democratic political institutions that are conducive to

citizen participation and involvement, the Gulf states are still backward not only in economic but

also political terms. Large populations have been excluded from the benefits of free trade and

markets.This is due largely to historical privations, to which one must add isolated geographies,

harsh climatic conditions, and public policies that have not been able to extend the benefits of

urbanization, literacy and communication to those areas. Poverty in Mexico is, in fact, closely

related to geography, although the exact characteristics of this relationship are still a topic that

merits much future research.

This paper has attempted to put geograhic determinants of development into perspective,

using mostly state-level information in the case of Mexico. However, the main findings point

towards the need to engage in a more local level of analysis that should first concentrate on

municipalities and then probably move on to localities within each political jurisdiction.

On the other hand, we have stressed the role of institutional ecology in the way in which

geography can determine development by promoting the creation of some types of political

institutions rather than others. While much of this remains a hypothesis rather than a proved

relationship, a very important area of future research constitutes being able to characterize local

political institutions at the municipal level in a meaningful way, seeking to find whether the

relationship with geography holds at that level of analysis.

Further studies should provide an empirical basis for formulating population policy. At

present, the Mexican population has largely settled in areas that are not geographically adequate.

Most Mexicans live at more than 3,000 feet above sea level. In addition, a large portion of the

population lives in mountainous areas with difficult communications, and a growing number is

invading the southern tropical rain forests. All of these settlement patterns indicate the existence

of institutions that are providing the wrong signals to social actors. This suggests that study

should be devoted to the feasibility of policies to improve health conditions along the coastline,

52

perhaps promoting migration towards the sea. This would involve promoting the “revolution” of

air conditioners and the construction of means of communications towards the coasts and the

borders, which seem to be natural areas for growth in the coming years.

The provision of public goods in new settlements on the coasts and lowlands would

probably require the creation of statutes, incentives and financial instruments to facilitate a

gradual population flow into places with adequate services. The results we have provided in the

paper show that urbanization and its correlates (electricity, sewage and water) play a key role in

the development prospect, controlling for other variables. The way in which geographic

conditions might make the provision of local public goods more or less difficult is a topic that

merits futher research.

Recent political developments in Mexico point towards a trend for a greater

decentralization of decision-making to the municipal levels, especially on those issues connected

with fiscal sovereignty and regulation. This is another major area of future research, since one

can think of policies that might promote the competition of different institutional models

prevailing in different regions. For example, one can imagine that innovative policies that

sacrifice current revenue collection in order to reward efficiency and later increase future tax

collection could be strategically proposed by municipal authorities and then, when they are

successful, become diffused through imitation.

An additional area that merits further research, and one with important policy

implications, is the need for ecology audits (both natural and institutional) to understand the

hidden and overt costs that Mexico is paying for maintaining its current set of institutions and

exploiting its natural resources. This should be done at the state and municipal levels, as well as

regionally, in order to understand interjurisdictional spillover effects.

But the most prominent area where we believe a fruitful analysis of the effects of

geography in development should be carried out is poverty and geographic poverty traps.

Poverty is usually concentrated in specific places, which share common characteristics not only

in the relative deprivation of public goods and services, but also in the natural resources and

political institutions that prevail. Understanding whether there are policy prescriptions of what

needs to be done to open up those poverty traps, thus removing barriers to many Mexicans’

sharing in the benefits of an open economy, is one of the greatest tasks ahead.

53

Those barriers could be natural, such as rivers, forests or mountains, or linguistic and

cultural, but they could well be man-made in terms of unintended effects of current public good

provision policies and even poverty relief efforts. These barriers might also represent a

byproduct of greater decentralization, which might entrench predatory political elites in poor

regions. If researchers could gain further insight into the way in which poverty and destitution

can be alleviated within a sophisticated understanding of geographic conditions and local public

goods, they might have important prescriptions to offer to governments and policymakers in the

most important topic for social development.

54

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57

Appendix. Descriptive Profile of GDP and Geography in Mexico and Full Set of Results forSection 5

Table 1. Mexican Regional GDP by Latitude BandState State GDP 97 Population 97 TOTAL per capita GDPLatitude28-32Baja California 19568227.82 2249968 7,555.74Sonora 21041600.68 2157252Chihuahua 14546691.24 2892725TOTAL 55,156,519.75 7,299,945

24-28Baja California Sur 3714582.643 398437 9,466.16Sinaloa 14117438.54 2478535Durango 9600107.839 1454979Coahuila 22140023.74 2225752Nuevo León 51582303.9 3680565Tamaulipas 20402011.77 2602891TOTAL 121,556,468.43 12,841,159

20-24Nayarit 5791387.523 916270 6,479.94Zacatecas 6447420.395 1372087Jalisco 52495308.18 6241683Aguascalientes 7028804.284 900551Guanajuato 15794298.38 4588751San Luis Potosí 17624673.11 2275205Querétaro 10719840.48 1309470Hidalgo 12656466.73 2184178Yucatán 10084780.46 1607534TOTAL 138,642,979.54 21,395,729

16-20Colima 5025784.63 502887 9,066.91Michoacán 18247845.66 3997565México 83899081.79 12198634DF 191902943.4 8519305Morelos 12834899.05 1511287Tlaxcala 4438425.173 916800Puebla 25682516.08 4875158Veracruz 39371891.79 7090128Guerrero 27795199.84 3049167Oaxaca 13668961.72 3420659Tabasco 11769477.8 1824104Chiapas 23396154.57 3851555Campeche 13060255.18 671343Quintana Roo 11225134.37 766895TOTAL 482,318,571.01 53,195,487

58

Table 2. Mexican Regional GDP by TemperatureState State GDP 97 Population 97 TOTAL per capita GDPTemperature10-18Aguascalientes 7028804.284 900551 12,497.05DF 191902943.4 8519305Edo México 83899081.79 12198634Querétaro 10719840.48 1309470Tlaxcala 4438425.173 916800TOTAL 297,989,095.10 23,844,760

10-22Chihuahua 14546691.24 2892725 6,632.81Coahuila 22140023.74 2225752Durango 9600107.839 1454979Jalisco 52495308.18 6241683Hidalgo 12656466.73 2184178San Luis Potosí 17624673.11 2275205Zacatecas 6447420.395 1372087Guanajuato 15794298.38 4588751Morelos 12834899.05 1511287TOTAL 164,139,888.66 24,746,647

10-26Chiapas 23396154.57 3851555 6,873.36Puebla 25682516.08 4875158Sonora 21041600.68 2157252Veracruz 39371891.79 7090128Michoacán 18247845.66 3997565Guerrero 27795199.84 3049167Nuevo León 51582303.9 3680565Oaxaca 13668961.72 3420659TOTAL 220,786,474.23 32,122,049

18-26Baja California S 3714582.643 398437 7,982.54Baja California 19568227.82 2249968Colima 5025784.63 502887Quintana Roo 11225134.37 766895Tamaulipas 20402011.77 2602891Sinaloa 14117438.54 2478535Yucatán 10084780.46 1607534Nayarit 3714582.643 398437TOTAL 87,852,542.88 11,005,584

26 or moreCampeche 13060255.18 671343 9,950.01Tabasco 11769477.8 1824104TOTAL 24,829,732.98 2,495,447

59

Table 3. Mexican Regional GDP by Ranges of RainfallState State GDP 97 Population 97 TOTAL per capita GDP

Rainfall0-600Baja California 19568227.82 2249968 8,592.03Baja California S 3714582.643 398437Coahuila 22140023.74 2225752Sonora 21041600.68 2157252Zacatecas 6447420.395 1372087Aguascalientes 7028804.284 900551TOTAL 79,940,659.57 9,304,047

300-1000Querétaro 10719840.48 1309470 7,842.86San Luis Potosí 17624673.11 2275205Guanajuato 15794298.38 4588751Tlaxcala 4438425.173 916800Nuevo León 51582303.9 3680565TOTAL 100,159,541.04 12,770,791

300-2000Colima 5025784.63 502887 10,000.27Campeche 13060255.18 671343Chihuhua 14546691.24 2892725DF 191902943.4 8519305Durango 9600107.839 1454979México 83899081.79 12198634Jalisco 52495308.18 6241683Quintana Roo 11225134.37 766895Tamaulipas 20402011.77 2602891Sinaloa 14117438.54 2478535Yucatán 10084780.46 1607534Nayarit 3714582.643 398437Michoacán 18247845.66 3997565Guerrero 27795199.84 3049167Morelos 12834899.05 1511287TOTAL 488,952,064.56 48,893,867

300-4000Chiapas 23396154.57 3851555 5,443.80Hidalgo 12656466.73 2184178Puebla 25682516.08 4875158Tabasco 11769477.8 1824104Veracruz 39371891.79 7090128Oaxaca 13668961.72 3420659TOTAL 126,545,468.68 23,245,782

60

Table 4. Regional GDP / squared km.

GDP 1997 km 2 GDP / km 2México 83,899,081.79 21,355 3,928,779.29Morelos 12,834,899.05 4,950 2,592,908.90Aguascalientes 7,028,804.28 5,471 1,284,738.49Tlaxcala 4,438,425.17 4,016 1,105,185.55Colima 5,025,784.63 5,191 968,172.73Querétaro 10,719,840.48 11,449 936,312.38Nuevo León 51,582,303.90 64,924 794,502.86Puebla 25,682,516.08 33,902 757,551.65Jalisco 52,495,308.18 80,836 649,405.07Hidalgo 12,656,466.73 20,813 608,103.91Veracruz 39,371,891.79 71,699 549,127.49Guanajuato 15,794,298.38 30,491 517,998.70Tabasco 11,769,477.80 25,627 459,260.85Guerrero 27,795,199.84 64,281 432,401.48Chiapas 23,396,154.57 74,211 315,265.32Michoacán 18,247,845.66 59,928 304,496.16Baja California 19,568,227.82 69,921 279,861.96San Luis Potosí 17,624,673.11 63,068 279,455.08Yucatán 10,084,780.46 38,402 262,610.81Campeche 13,060,255.18 50,812 257,030.92Tamaulipas 20,402,011.77 79,384 257,004.08Sinaloa 14,117,438.54 58,328 242,035.36Quintana Roo 11,225,134.37 50,212 223,554.82Nayarit 5,791,387.52 26,979 214,662.79Coahuila 22,140,023.74 149,982 147,617.87Oaxaca 13,668,961.72 93,952 145,488.78Sonora 21,041,600.68 182,052 115,580.17Zacatecas 6,447,420.40 73,252 88,016.99Durango 9,600,107.84 123,181 77,934.97Chihuahua 14,546,691.24 244,938 59,389.28Baja California Sur 3,714,582.64 73,475 50,555.74

61

Table 5. Mexican Regional GDP by Index of Trade MobilityState GDP Population Total per capita GDP

0-15Chiapas 23,396,154.57 3,851,555 7,267.73Oaxaca 13,668,961.72 3,420,659Zacatecas 6,447,420.40 1,372,087Guerrero 27,795,199.84 3,049,167Campeche 13,060,255.18 671,343Durango 9,600,107.84 1,454,979Quintana Roo 11,225,134.37 766,895Baja California S 3,714,582.64 398,437Total 108,907,816.56 14,985,122

15-25San Luis Potosí 17,624,673.11 2,275,205 7,662.31Veracruz 39,371,891.79 7,090,128Tabasco 11,769,477.80 1,824,104Yucatán 10,084,780.46 1,607,534Michoacán 18,247,845.66 3,997,565Nayarit 5,791,387.52 916,270Tamaulipas 20,402,011.77 2,602,891Jalisco 52,495,308.18 6,241,683Chihuahua 14,546,691.24 2,892,725Coahuila 22,140,023.74 2,225,752Nuevo León 51,582,303.90 3,680,565Sonora 21,041,600.68 2,157,252Baja California 19,568,227.82 2,249,968Total 304,666,223.67 39,761,642

25-35Puebla 25,682,516.08 4,875,158 4,831.35Hidalgo 12,656,466.73 2,184,178Guanajuato 15,794,298.38 4,588,751Sinaloa 14,117,438.54 2,478,535Total 68,250,719.72 14,126,622

35 or moreTlaxcala 4,438,425.17 916,800 7,148.18Querétaro 10,719,840.48 1,309,470México 83,899,081.79 12,198,634Morelos 12,834,899.05 1,511,287Aguascalientes 7,028,804.28 900,551Colima 5,025,784.63 502,887Total 123,946,835.40 17,339,629

62

Full Set of Results for Section 5

Table 5.1. Geographic Determinants of State GDP Levels, 1993Dependent Variable: LPIB93

(1) (2) (3) (4) (5)Cons 4.054627

(1.625)4.064782(-1.661)

4.113786(1.565)

4.103227(18.195)

3.783938(10.216)

Rainavg -0.00068(-1.243)

-0.00081(0.855)

-0.0013(-2.743)

-0.00131(-2.892)

-0.00084(-1.569)

Rain2 1.25E-07(0.591)

1.67E-07(-0.332)

3.12E-07(1.579)

3.30E-07(1.786)

2.34E-07(1.213)

Tempavg -0.08377(-0.356)

-0.07705(0.508)

-0.02089(-0.086)

Temp2 0.002895(0.511)

0.002838(2.503)

0.000934(0.16)

Coast 7.07E-05(0.588)

Border 0.432294(2.533)

0.41256(3.375)

0.392953(2.415)

LnDensity 0.206533(3.372)

0.201973(1.654)

0.223623(3.388)

0.218667(3.968)

0.168632(2.393)

Landlock -0.28603(-1.736)

-0.338(-2.416)

F 4.08 4.85 3.87 6.06 6.55

R2 0.4349 0.4518 0.3805 0.4195 0.4964

Table 5.2. Geographic Determinants of State GDP Levels 1993,Including InfrastructureDependent Variable: LPIB93

(1) (2) (3) (4) (5) (6) (7) (8) (9)Cons 3.783938

(10.216)2.703537(8.94)

1.176299(1.17)

2.113241(3.378)

2.672633(7.933)

2.861849(3.35)

2.583502(6.124)

2.574741(8.57)

3.717788(3.901)

Rainavg -0.00084(-1.569)

-0.00033(-1.068)

-0.00057(-1.123)

-0.00049(-1.108)

-0.00019(-0.515)

-0.00034(-1.032)

-0.00032(-1.02)

-0.0002(-0.598)

-0.00019(-0.54)

Rain2 2.34E-07(1.213)

1.48E-07(1.353)

1.88E-07(1.154)

2.13E-07(1.402)

7.20E-09(0.06)

1.48E-07(1.289)

1.49E-07(1.373)

6.03E-08(0.455)

-7.09E-11(0)

Border 0.392953(2.415)

0.018401(0.161)

0.356318(2.247)

0.33832(2.17)

0.296489(2.341)

0.010628(0.086)

0.029883(0.241)

0.135006(0.771)

0.12932(0.735)

LnDensity 0.168632(2.393)

0.02822(0.465)

0.110946(1.455)

0.112536(1.672)

0.061782(0.968)

0.028481(0.46)

0.02892(0.464)

0.032464(0.519)

0.033703(0.518)

Urban 1.953585(5.325)

2.007929(4.017)

1.864856(3.322)

1.064467(1.352)

1.089266(1.322)

Elect 2.864115(2.84)

-0.20689(-0.187)

-0.76987(-0.629)

Agua 1.903167(3.126)

0.192635(0.301)

-0.97795(-0.731)

Dren 1.678719(6.248)

0.937332(1.553)

1.555829(1.576)

F 6.55 13.32 16.29 10.98 21.33 10.35 14.38 16.5 11.66R2 0.4964 0.7073 0.5743 0.5969 0.7085 0.7075 0.7079 0.7297 0.7433

63

Table 5.3. Geographic Determinants of State GDP Levels 1993, Including Literacyand Indigenous Population

Dependent Variable: LPIB93(1) (2) (3)

Cons 2.703537(8.94)

3.179507(-0.18)

3.336482(9.408)

Rainavg -0.00033(-1.068)

-6.3E-05(0.537)

-4.7E-05(-0.13)

Rain2 1.48E-07(1.353)

6.16E-08(0.567)

4.81E-08(0.401)

Border 0.018401(0.161)

0.087662(0.851)

0.059486(0.4)

Density 0.02822(0.465)

0.054196(2.212)

0.065749(1.055)

Urban 1.953585(5.325)

1.222225(-2.218)

1.096808(1.91)

Femill -1.826(9.718)

-3.07582(-1.833)

Lengua 4.898(1.023)

F 13.32 19.96 11.69R2 0.7073 0.7416 0.7538

Table 5.4. Geographic Determinants of State GDP Levels 1993, Municipal Effects

Dependent Variable: LPIB93

(1) (2) (3) (4) (5) (6) (7)Cons 1.37908

(11.306)1.46203(9.833)

1.55172(9.241)

1.59739(10.099)

1.509947(10.676)

1.35786(10.066)

1.422119(8.999)

Rainavg -.0005331(-4.551)

-.0004249(-2.777)

-.0003565(-2.383)

-.0003406(-2.274)

-.0004145(-2.785)

-.0003838(-3.420)

-.0003435(-2.940)

Rain2 3.09e-07(6.795)

2.73e-07(5.097)

2.51e-07(4.806)

2.39e-07(4.494)

2.62e-07(4.927)

2.58e-07(6.530)

2.45e-07(5.948)

Border .056026(1.098)

.092574(1.461)

0.770224(1.357)

.084035(1.560)

.093363(1.618)

.039856(0.845)

.029569(0.669)

Density .0000551(4.043)

.0000621(3.836)

.0000564(4.087)

.0000563(4.613)

.0000604(4.300)

.0000725(6.539)

.0000665(7.054)

Urban .694243(4.387)

.519593(2.518)

.4413085(2.048)

.387566(1.931)

.476538(2.487)

.681575(3.783)

.629276(3.141)

Lengua -1.43437(-2.154)

-1.08514(-1.259)

-1.29613(-1.941)

Femill -.669026(-2.472)

-.59494(-2.420)

-.48519(-1.869)

Munic -.0001663(-1.904)

-.0001317(-1.295)

Munpc -1058.636(-1.992)

-1117.851(-1.896)

Munkm -15.02066(-6.309)

-13.76061(-4.812)

F 102.20 241.99 233.36 143.80 97.78 203.05 185.03R2 0.8422 0.8665 0.8777 0.8968 0.8849 0.9094 0.9119

64

Table 5.5. Geographic Determinants of State GDP Levels 1993,Municipal Effects 1825

Dependent Variable: LPIB93

(1) (2) (3)Cons 1.37840

(11.218)1.52254(9.174)

1.43842(9.962)

Rainavg -.0004417(-3.387)

-.0003558(-2.476)

-.0004076(-2.751)

Rain2 2.72e-07(5.344)

2.49e-07(4.982)

2.65e-07(5.134)

Border .059150(1.163)

.067619(1.259)

.081882(1.367)

Density .0000534(4.186)

.0000543(4.206)

.0000595(3.931)

Urban .655766(4.213)

.477489(2.283)

.545052(2.782)

Femill -.624572(-2.301)

Lenind -1.442691(-1.990)

Muni1825 -.0005866(-2.534)

-.0003494(-1.616)

-.0004737(-2.375)

F 155.58 172.08 160.18R2 0.8626 0.8801 0.8730

Table 5.6. Dependent Variable: fgt22(1) (2) (3) (4)

Cons .168338(0.287)

.121371(0.205)

.164487(0.288)

.1210375(2.314)

Rainavg .0001752(1.151)

.000254(2.384)

.0001725(1.621)

.0001852(1.688)

Rain2 -2.09e-07(-0.370)

-4.35e-08(-0.951)

-2.00e-08(-0.456)

-3.97e-08(-0.870)

Tempavg .0084413(0.143)

.0004635(0.008)

.0089126(0.156)

Temp2 -.0005194(-0.359)

-.00021(-0.147)

-.0005283(-0.375)

Coast 1.23e-06(0.048)

Border -.0735318(-2.869)

-.073862(-3.436)

-.0684467(-2.840)

Density -.0003827(-2.952)

-.0004304(-3.103)

-.0003822(-3.108)

-.0002447(-2.443)

Landlock .060328(2.082)

F 11.57 8.35 9.30 9.46R2 0.6337 0.6164 0.6336 0.5068

65

Table 5.7. Dependent Variable: fgt22(1) (2) (3) (4) (5) (6) (7) (8)

Cons .521036(7.863)

.853163(3.870)

.656646(6.431)

.548774(8.033)

.506230(7.269)

.697858(3.798)

1.146895(4.335)

.637288(2.898)

Rainavg .0000887(1.488)

.0000768(1.363)

.0000723(1.346)

.0000479(0.868)

.0000415(0.634)

.0000914(1.056)

.0001055(1.126)

.0000491(0.840)

Rain2 -2.76e-08(-0.989)

-2.55e-08(-1.121)

-2.90e-08(-1.102)

8.01e-08(0.032)

1.30e-08(0.524)

-3.73e-08(-1.064)

-2.82e-08(-0.892)

5.41e-09(0.187)

Border .036904(1.423)

.020375(0.763)

.024485(0.978)

-.006543(-0.214)

-.042554(-1.770)

-.050251(-2.078)

-.054197(-1.905)

-.010806(-0.312)

Density .0001002(1.301)

.0001136(1.482)

.0001008(1.372)

.0000659(0.960)

8.57e-07(0.017)

-.0000704(-0.967)

-.0000318(-0.413)

.0000697(0.924)

Urban -.550246(-5.629)

-.4336787(-3.278)

-.451181(-3.793)

-.232484(-1.581)

-.209343(-1.226)

Elect -.434733(-1.487)

-1.06982(-3.901)

-.235433(-0.667)

Agua -.218828(-1.387)

-.607940(-3.079)

.151433(0.691)

Dren -.316914(-2.717)

-.471810(-6.312)

-.345406(-1.924)

F 14.12 12.46 13.30 14.46 16.31 9.03 21.06 10.97R2 0.7921 0.8119 0.8066 0.8394 0.8225 0.6896 0.7043 0.8444

Table 5.8. Dependent Variable: fgt22(1) (2) (3)

Cons .372475(3.462)

.441444(5.714)

.4029722(3.655)

Rainavg -.0000103(-0.187)

.0000226(0.453)

8.41e-07(0.015)

Rain2 3.26e-09(0.163)

-9.36e-09(-0.532)

-1.87e-09(-0.091)

Border .021144(0.976)

.0059445(0.247)

.008509(0.361)

Density .0000496(0.891)

.0000879(1.274)

.0000704(1.146)

Urban -.348977(-2.706)

-.3943003(-3.912)

-.3563798(-2.713)

Femill .562566(2.740)

.2338118(0.879)

Lengua 1.87271(4.920)

1.319963(2.502)

F 23.51 52.67 44.32R2 0.8568 0.8677 0.8724

66

Table 5.9. Dependent Variable: fgt22(1) (2) (3) (4) (5) (6)

Cons .521036(7.863)

.372558(3.578)

.343522(3.405)

.405195(5.422)

.4522231(5.571)

.402986(3.656)

Rainavg .0000887(1.488)

-.0000418(-0.745)

-.0000381(-.0732)

-6.15e-06(-0.119)

2.77e-06(0.064)

-.0000174(-0.352)

Rain2 -2.76e-08(-0.989)

1.82e-08(0.855)

1.93e-08(0.925)

6.86e-09(0.362)

4.98e-10(0.033)

8.56e-09(0.439)

Border .036904(1.423)

.006857(0.306)

.0098163(0.453)

.0000514(0.002)

.0151645(0.646)

.029022(1.306)

Density .0001002(1.301)

.0000339(0.668)

.0000471(1.001)

.0000786(1.454)

-.0000149(-0.311)

-.0000392(-0.560)

Urban -.550246(-5.629)

-.321179(-2.611)

-.301272(-2.536)

-.349134(-3.610)

-.409791(-3.876)

-.3848575(-2.870)

Lengua 1.419545(3.231)

1.60672(4.783)

Femill .426679(2.032)

.439119(2.208)

.443549(2.219)

Munic .0001788(3.393)

Munpc 731.9381(3.086)

664.549(2.495)

Munkm 7.507978(6.650)

7.14284(4.707)

F 14.12 58.82 27.58 37.76 87.30 20.99R2 0.7921 0.8838 0.9004 0.9017 0.9076 0.8915

Table 5.10. Geographical Determinants of Per Capita GDP Growth (1950-1993)

Lnpib50 -0.5488505(-7.199)

-0.5965216(-7.731)

-0.5532859(-9.945)

-0.5454843(-9.572)

-0.5758974(-8.277)

Rainavg -0.0014015(-3.664)

-0.0013259(-3.432)

-0.0012764(-3.772)

-0.0013783(-4.442)

-0.0013361(-4.031)

Rain2 6.81E-07(5.38)

6.58E-07(4.954)

6.45E-07(5.49)

6.95E-07(6.214)

6.80E-07(5.547)

Tempavg -0.0897579(-0.617)

-0.0971559(-0.669)

-0.073668(-0.538)

Temp2 0.0024662(0.732)

0.002595(0.764)

0.0020618(0.647)

Coast -0.0001007(-1.178)

-0.0000805(-1.061)

Border -0.0088027(-0.068)

0.071877(0.672)

0.0356931(0.376)

Landlock 0.0568391(0.564)

Lndens50 0.0877558(2.368)

0.1115465(2.566)

0.0947465(2.311)

0.0886773(4.285)

0.1082454(4.438)

_cons 3.853566(2.605)

3.92258(2.645)

3.558483(2.603)

3.013644(10.749)

2.987933(10.489)

F 78.14 48.07 56.48 59.91 51.02R2 0.8979 0.8912 0.8907 0.8914 0.886

67

Table 5.11. Geographical Determinants of Per Capita GDP Growth (1950-1993), IncludingHuman Capital

lnpib50 -0.5669299(-6.791)

-0.5860826(-6.151)

-0.6031166(-7.306)

Rainavg -0.0013487(-3.718)

-0.0012693(-3.217)

-0.0014186(-4.145)

rain2 6.88E-07(5.523)

6.52E-07(4.683)

7.12E-07(6.418)

Coast -0.0000743(-0.89)

-0.0000997(-1.086)

-0.0000908(-1.234)

lndens50 0.082908(2.536)

0.0770735(2.063)

0.0846422(2.675)

urb60 0.0979985(0.278)

-0.0066703(-0.017)

-0.0042092(-0.012)

iprim40 0.318186(0.61)

iprim59 0.4704316(1.562)

_cons 3.026254(11.084)

3.02546(11.342)

2.91656(10.374)

F 46.42 41.15 87.11R2 0.8918 0.8943 0.9002

Table 5.12. Geographical Determinants of per capita GDP Growth (1950-1993), MunicipalEffects

(1) (2) (3)Lnpib50 -0.5709212

(-5.242)-0.5496595(-7.087)

-0.6000654(-5.247)

Rainavg -0.0012228(-4.271)

-0.0011128(-4.268)

-0.0014283(-3.933)

rain2 6.53E-07(7.154)

6.09E-07(6.983)

7.16E-07(6.013)

Coast -0.0002487(-1.715)

-0.0002162(-1.379)

-0.0000916(-1.196)

lndens50 0.0691924(0.989)

0.0637001(2.055)

0.082734(1.785)

urb60 0.0203156(0.057)

0.0176345(0.051)

-0.009359(-0.024)

iprim59 0.4292524(1.655)

0.2185105(0.502)

0.4708052(1.53)

lmun17km 0.0016033(0.017)

-0.0506374(-0.788)

Lnmunkm 0.0033509(0.055)

_cons 2.803242(4.729)

3.004297(5.991)

2.943211(5.863)

F 110.65 77.97 73.22R2 0.9271 0.9335 0.9002

68

Table 5.13. Geographic Determinants of State GDPLevels 1980Dependent Variable: LPIB80

(1) (2) (3) (4) (5)Cons 2.4629

(1.0773)2.4535(2.349)

2.52395(2.203)

2.060947(34.363)

1.88822(18.282)

Rainavg -.000535(-2.497)

-.000544(-3.019)

-.0007371(-4.736)

-.0007399(-4.841)

-.0005814(-3.220)

Rain2 2.63e-07(3.223)

2.65e-07(3.732)

3.22e-07(4.697)

3.28e-07(4.838)

2.49e-07(4.217)

Tempavg -.06681(-0.621)

-.06558(-.0633)

-.04629(-0.421)

Temp2 .001831(0.707)

.001809(0.722)

.001143(0.435)

Coast 3.85e-06(0.086)

Border .179146(2.734)

.178091(2.775)

.168723(2.775)

Density .0001161(5.868)

.0001164(6.059)

.0001183(5.689)

.0001251(11.465)

.0001169(12.281)

Landlock -.09306(-1.496)

-.09039(-1.809)

F 26.33 31.98 25.89 37.45 38.48R2 0.7049 0.7048 0.6325 0.6282 0.6782

Table 5.14. Geographic Determinants of State GDP Levels 1980, IncludingInfrastructure

Dependent Variable: LPIB80(1) (2) (3) (4) (5) (6) (7) (8) (9)

Cons 1.88822(18.282)

1.37908(11.306)

.654662(1.665)

1.07292(4.190)

1.36311(10.059)

1.31556(4.048)

1.23523(6.706)

1.32838(10.855)

1.61689(4.386)

Rainavg -.0005814(-3.220)

-.0005331(-4.551)

-.0005309(-2.876)

-.0004917(-3.014)

-.0004427(-2.900)

-.0005314(-4.287)

-.0005161(-4.143)

-.0004723(-3.201)

-.0004674(-3.202)

Rain2 2.94e-07(4.217)

3.09e-07(6.795)

2.99e-07(4.891)

3.08e-07(5.297)

2.46e-07(4.712)

3.09e-07(6.615)

3.11e-07(6.906)

2.70e-07(4.510)

2.59e-07(3.913)

Border .168723(2.775)

.056026(1.098)

.164028(2.664)

.154783(2.642)

.149048(2.742)

.05967(1.036)

.105659(1.410)

.100674(1.287)

Density .0001169(12.281)

.0000551(4.043)

.0000905(10.324)

.0000872(10.241)

.0000685(7.936)

.0000555(3.895)

.0000565(3.967)

.0000586(4.407)

.0000574(4.377)

Urban .694243(4.387)

.669891(3.168)

.587412(2.793)

.312103(0.978)

.336866(0.999)

Elect 1.28327(3.390)

.08467(0.212)

-.333352(-0.610)

Agua .85656(3.358)

.233442(0.942)

-.086214(-0.208)

Dren .639021(5.580)

.402743(1.599)

.519269(1.365)

F 38.48 102.20 71.35 95.58 163.93 87.72 95.15 118.98 115.43R2 0.6782 0.8422 0.7683 0.7859 0.8524 0.8424 0.8463 0.8617 0.8651

69

Table 5.15. Geographic Determinants of State GDP Levels 1980, Including Literacy andIndigenous PopulationDependent Variable: LPIB80

(1) (2) (3)Cons 1.37908

(11.306)1.556826(9.516)

1.556245(9.249)

Rainavg -.0005331(-4.551)

-.0003813(-2.788)

-.0003817(-2.714)

Rain2 3.09e-07(6.795)

2.62e-07(5.640)

2.62e-07(5.524)

Border .056026(1.098)

.068052(1.287)

.068576(1.249)

Density .0000551(4.043)

.0000554(4.293)

.0000556(3.874)

Urban .694243(4.387)

.454746(2.191)

.454435(2.134)

Femill -.771651(-2.965)

-.761538(-1.786)

Lengua -.042127(-0.035)

F 102.20 150.72 124.81R2 0.8422 0.8740 0.8740

Table 5.16. Geographic Determinants of State GDP Levels 1980, Municipal EffectsDependent Variable: LPIB80

(1) (2) (3) (4) (5) (6) (7)Cons 1.37908

(11.306)1.46203(9.833)

1.55172(9.241)

1.59739(10.099)

1.509947(10.676)

1.35786(10.066)

1.422119(8.999)

Rainavg -.0005331(-4.551)

-.0004249(-2.777)

-.0003565(-2.383)

-.0003406(-2.274)

-.0004145(-2.785)

-.0003838(-3.420)

-.0003435(-2.940)

Rain2 3.09e-07(6.795)

2.73e-07(5.097)

2.51e-07(4.806)

2.39e-07(4.494)

2.62e-07(4.927)

2.58e-07(6.530)

2.45e-07(5.948)

Border .056026(1.098)

.092574(1.461)

0.770224(1.357)

.084035(1.560)

.093363(1.618)

.039856(0.845)

.029569(0.669)

Density .0000551(4.043)

.0000621(3.836)

.0000564(4.087)

.0000563(4.613)

.0000604(4.300)

.0000725(6.539)

.0000665(7.054)

Urban .694243(4.387)

.519593(2.518)

.4413085(2.048)

.387566(1.931)

.476538(2.487)

.681575(3.783)

.629276(3.141)

Lengua -1.43437(-2.154)

-1.08514(-1.259)

-1.29613(-1.941)

Femill -.669026(-2.472)

-.59494(-2.420)

-.48519(-1.869)

Munic -.0001663(-1.904)

-.0001317(-1.295)

Munpc -1058.636(-1.992)

-1117.851(-1.896)

Munkm -15.02066(-6.309)

-13.76061(-4.812)

F 102.20 241.99 233.36 143.80 97.78 203.05 185.03R2 0.8422 0.8665 0.8777 0.8968 0.8849 0.9094 0.9119